cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.23641/asha.26487376
title: Statistical learning in language disorder (Kapa & Mettler, 2024)

description: <b>Purpose: </b>Our goal was to compare statistical learning abilities between preschoolers with developmental language disorder (DLD) and peers with typical development (TD) by assessing their learning of two artificial grammars.<b>Method: </b>Four- and 5-year-olds with and without DLD were compared on their statistical learning ability using two artificial grammars. After learning an <i>aX</i> grammar, participants learned a relatively more complex <i>abX</i> grammar with a nonadjacent relationship between <i>a</i> and <i>X</i>. Participants were tested on their generalization of the grammatical pattern to new sequences with novel <i>X</i> elements that conformed to (<i>aX, abX</i>) or violated (<i>Xa, baX</i>) the grammars.<b>Results: </b>Results revealed an interaction between age and language group. Four-year-olds with and without DLD performed equivalently on the <i>aX</i> and <i>abX</i> grammar tests, and neither of the 4-year-old groups’ accuracy scores exceeded chance. In contrast, among 5-year-olds, TD participants scored significantly higher on <i>aX</i> tests compared to participants with DLD, but the groups’ <i>abX</i> scores did not differ. Five-year-old participants with DLD did not exceed chance on any test, whereas 5-year-old TD participants’ scores exceeded chance on all grammar learning outcomes. Regression analyses indicated that <i>aX</i> performance positively predicted learning outcomes on the subsequent <i>abX</i> grammar for TD participants.<b>Conclusion: </b>These results indicate that preschool-age participants with DLD show deficits relative to typical peers in statistical learning, but group differences vary with participant age and type of grammatical structure being tested.<b>Supplemental Material S1.</b> Pearson correlations between executive function scores and artificial grammar learning.Kapa, L. L., & Mettler, H. M. (2024). Statistical learning among preschoolers with and without developmental language disorder: Examining effects of language status, age, and prior learning. <i>Journal of Speech, Language, and Hearing Research</i>, <i>67</i>(9), 3081–3093. https://doi.org/10.1044/2024_JSLHR-23-00602
DOI: 10.23641/asha.26487376.v1
title: Statistical learning in language disorder (Kapa & Mettler, 2024)

description: <b>Purpose: </b>Our goal was to compare statistical learning abilities between preschoolers with developmental language disorder (DLD) and peers with typical development (TD) by assessing their learning of two artificial grammars.<b>Method: </b>Four- and 5-year-olds with and without DLD were compared on their statistical learning ability using two artificial grammars. After learning an <i>aX</i> grammar, participants learned a relatively more complex <i>abX</i> grammar with a nonadjacent relationship between <i>a</i> and <i>X</i>. Participants were tested on their generalization of the grammatical pattern to new sequences with novel <i>X</i> elements that conformed to (<i>aX, abX</i>) or violated (<i>Xa, baX</i>) the grammars.<b>Results: </b>Results revealed an interaction between age and language group. Four-year-olds with and without DLD performed equivalently on the <i>aX</i> and <i>abX</i> grammar tests, and neither of the 4-year-old groups’ accuracy scores exceeded chance. In contrast, among 5-year-olds, TD participants scored significantly higher on <i>aX</i> tests compared to participants with DLD, but the groups’ <i>abX</i> scores did not differ. Five-year-old participants with DLD did not exceed chance on any test, whereas 5-year-old TD participants’ scores exceeded chance on all grammar learning outcomes. Regression analyses indicated that <i>aX</i> performance positively predicted learning outcomes on the subsequent <i>abX</i> grammar for TD participants.<b>Conclusion: </b>These results indicate that preschool-age participants with DLD show deficits relative to typical peers in statistical learning, but group differences vary with participant age and type of grammatical structure being tested.<b>Supplemental Material S1.</b> Pearson correlations between executive function scores and artificial grammar learning.Kapa, L. L., & Mettler, H. M. (2024). Statistical learning among preschoolers with and without developmental language disorder: Examining effects of language status, age, and prior learning. <i>Journal of Speech, Language, and Hearing Research</i>, <i>67</i>(9), 3081–3093. https://doi.org/10.1044/2024_JSLHR-23-00602
DOI: 10.15468/dl.ndqzax
title: Occurrence Download

description: A dataset containing 583 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is one of (ARG, CHL, PRY, URY, FLK, BRA.21_1, BRA.16_1, BRA.24_1)",
 "TaxonKey is Tripodanthus (Eichler) Tiegh."
 ]
}

 The dataset includes 583 records from 67 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015471-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5064/f6d0ybea/uvdoub
title: Vedanthan_BIGPIC_Informedconsentform_Baraza_20141218.pdf
DOI: 10.5064/f6d0ybea/3wjap6
title: Vedanthan_BIGPIC_Informedconsent form_FGDs_20141218.pdf
DOI: 10.5064/f6d0ybea/cuau4y
title: Vedanthan_DataNarrative.pdf
DOI: 10.15468/dl.cmy7wc
title: Occurrence Download

description: A dataset containing 3047 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Colombia",
 "TaxonKey is Cenchrus clandestinus (Hochst. ex Chiov.) Morrone"
 ]
}

 The dataset includes 3047 records from 47 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015472-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5064/f6d0ybea/tsd2nh
title: README_Vedanthan.txt
DOI: 10.15468/dl.nawewt
title: Occurrence Download

description: A dataset containing 2073 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil)"
 ]
 }
 },
 "TaxonKey is Terminalia sericea Burch. ex DC.",
 "Continent is Africa"
 ]
}

 The dataset includes 2073 records from 67 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015474-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5064/f6d0ybea/bbph60
title: Vedanthan_BIGPIC_Aim1CodesDefinitions_Excerpts20210415.tab
DOI: 10.5064/f6d0ybea/ybcyet
title: Vedanthan_BIGPIC Informed consent form_FGDs_20141218.pdf
DOI: 10.5064/f6d0ybea/coay1d
title: Vedanthan_BIGPIC Informed consent form Baraza_20141218.pdf
DOI: 10.5064/f6d0ybea/zrdv7t
title: Vedanthan_Data Narrative.pdf
DOI: 10.5064/f6mncapq/gygkcs
title: Aguessivognon_DataNarrative.pdf
DOI: 10.5064/f6mncapq/amnvjf
title: Aguessivognon_InterviewGuide.pdf
DOI: 10.5064/f6mncapq/lepvvk
title: Aguessivognon_SociodemographicDataForm.pdf
DOI: 10.5064/f6mncapq/xy4yf0
title: Aguessivognon_CodingDefinitionExcerpts.pdf
DOI: 10.5064/f6mncapq/dvymh9
title: README_Aguessivognon.txt
DOI: 10.15468/dl.wnsmwd
title: Occurrence Download

description: A dataset containing 635 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil)"
 ]
 }
 },
 "TaxonKey is Uapaca kirkiana Müll.Arg.",
 "Continent is Africa"
 ]
}

 The dataset includes 635 records from 47 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015475-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.23641/asha.26376502
title: Auditory processing in preschool children (Sun et al., 2024)

description: <b>Objective: </b>This study aimed to investigate the characteristics of auditory processing (AP) in preschool children with attention-deficit/hyperactivity disorder (ADHD) using the speech auditory brainstem response (speech-ABR), which provides insights into the AP of speech signals in the central auditory nervous system (CANS).<b>Method: </b>A total of 84 preschool children diagnosed with ADHD, aged 4–6 years, were matched with 84 typically developing (TD) children based on gender and age. All children underwent speech-ABR testing, cognitive assessment using the Wechsler Preschool and Primary Scale of Intelligence–Fourth Edition or the Wechsler Intelligence Scale for Children–Fourth Edition, and a continuous performance test.<b>Results: </b>Children with ADHD exhibited significantly longer latencies of speech-ABR waveforms V, A, and D compared to TD children. Multiple linear regression analysis showed that the latencies of speech-ABR waves V, A, and D were affected by the presence of ADHD, but not by the full-scale intelligence quotient.<b>Conclusions: </b>This study revealed that preschool children with ADHD exhibited abnormal AP of speech signals in their CANS. The findings suggest that speech-ABR can be utilized as a reliable measure to evaluate AP ability in this population, as it remains unaffected by cognitive or attentional factors. The transient response (V, A) of speech-ABR was found to be a significant predictor of ADHD in a clinical setting. Early assessment of AP abnormalities via speech-ABR is recommended in preschool-age children to develop targeted interventions for ADHD.<b>Supplemental Material S1.</b> Pure-tone air-conduction threshold for all children.<b>Supplemental Material S2.</b> Latencies and amplitudes of speech-ABR measures for children with ADHD and TD children.<b>Supplemental Material S3.</b> Analysis on factors influencing the wave V of speech-ABR by multiple linear regression analysis.<b>Supplemental Material S4.</b> Analysis on factors influencing the wave A of speech-ABR by multiple linear regression analysis.<b>Supplemental Material S5.</b> Analysis on factors influencing the wave D of speech-ABR by multiple linear regression analysis.Sun, Y., Zhou, J., Zhu, H., Liu, P., Lin, H., Xiao, Z., Yu, X., Qian, J., Tong, M., Chi, X., & Hong, Q. (2024). Characteristics of speech auditory brainstem response in preschool children with attention-deficit/hyperactivity disorder. <i>Journal of Speech, Language, and Hearing Research</i>, <i>67</i>(9), 3163–3177. https://doi.org/10.1044/2024_JSLHR-23-00454
DOI: 10.5064/f6jkv8qz/ri3nif
title: Aguessivognon_Data-Narrative.pdf
DOI: 10.5064/f6jkv8qz/t4r24i
title: Aguessivognon_Interview-Guide.pdf
DOI: 10.5064/f6jkv8qz/rxengg
title: Aguessivognon_Coding_Definitions_Excerpts.pdf
DOI: 10.5064/f6jkv8qz/ekglw6
title: Aguessivognon_Sociodemographic-Data_Questionnaire.pdf
DOI: 10.5064/f6jkv8qz/zqjzs1
title: README_Aguessivognon.txt
DOI: 10.5064/f6gxxbvd/pcz2mi
title: Jennerich_Table.pdf
DOI: 10.5064/f6gxxbvd/tfio3p
title: Jennerich_IRBApproval.pdf
DOI: 10.5064/f6gxxbvd/c0u1ki
title: Jennerich_InterviewGuide.pdf
DOI: 10.5064/f6wyixv3/aqffue
title: Mazur_DatingOnline_CodingManual.pdf
DOI: 10.5064/f6wyixv3/ninmqu
title: Mazur_DatingOnline_Dataset.tab
DOI: 10.5064/f6wyixv3/hxj3dq
title: README_Mazur.txt
DOI: 10.5064/f6wyixv3/pf8qun
title: Mazur_DatingOnline_CodingManual.pdf
DOI: 10.5064/f6yya3o3/id8hvj
title: VandeVusse-Mueller_CognitiveInterviewConsentForm.pdf
DOI: 10.5064/f6yya3o3/saeiaj
title: VandeVusse-Mueller_DataSharingOverviewMethods.pdf
DOI: 10.5064/f6yya3o3/7n9fdo
title: README_VandeVusse-Mueller.txt
DOI: 10.5064/f6yya3o3/orozcv
title: VandeVusse-Mueller_DataSharingCodingScheme.tab
DOI: 10.5064/f6yya3o3/wyqbf2
title: VandeVusse-Mueller_DataSharingNodeAnalysis.tab
DOI: 10.5064/f6yya3o3/skbenp
title: VandeVusse-Mueller_CognitiveInterviewConsentForm.pdf
DOI: 10.5064/f6yya3o3/jhj18t
title: VandeVusse-Mueller_DataSharingOverviewMethods.pdf
DOI: 10.5064/f6zqwqjs/iacyzq
title: Havice_ODSCI-Metadata.xlsx
DOI: 10.5064/f6zqwqjs/bxh13v
title: Havice_ODSIC-CodingScheme.pdf
DOI: 10.5064/f6zqwqjs/2b0s8w
title: Havice_ODSIC.pdf
DOI: 10.5064/f6zqwqjs/7gj5n5
title: 0_README_Havice.txt
DOI: 10.5064/f6ymwpux/xoskfj
title: Harrison_s3_p13_clinician_RN_interview.pdf
DOI: 10.5064/f6ymwpux/oewig9
title: Harrison_s1_p24_leader_MD_interview.pdf
DOI: 10.5064/f6ymwpux/hzazup
title: Harrison_s3_p11_clinician_RN_interview.pdf
DOI: 10.5064/f6ymwpux/rk5ow4
title: Harrison_s4_p43_clinician_RN_interview.pdf
DOI: 10.5064/f6ymwpux/bynmqp
title: Harrison_s3_p1_leader_MD_interview.pdf
DOI: 10.5064/f6ymwpux/cksgia
title: Harrison_s2_p33_QI_RN_interview.pdf
DOI: 10.15468/dl.dfyqxw
title: Occurrence Download

description: A dataset containing 2086 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil)"
 ]
 }
 },
 "TaxonKey is Vangueria infausta Burch.",
 "Continent is Africa"
 ]
}

 The dataset includes 2086 records from 56 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015476-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5064/f6ymwpux/zs65rm
title: Harrison_s4_p50_clinician_RN_interview.pdf
DOI: 10.5064/f6ymwpux/sy7wwh
title: Harrison_s1_p17_leader_interview.pdf
DOI: 10.5064/f6ymwpux/sg4lmt
title: Harrison_s1_p15_QI_SW_interview.pdf
DOI: 10.5064/f6ymwpux/4zxxiy
title: Harrison_s4_p45_clinician_CP_interview.pdf
DOI: 10.5064/f6ymwpux/aw33lb
title: Harrison_s2_p29_leader_RN_interview.pdf
DOI: 10.5064/f6ymwpux/3hxn48
title: Harrison_s3_p3_QI_interview.pdf
DOI: 10.5064/f6ymwpux/acq5xw
title: Harrison_s3_p10_clinician_sw_interview.pdf
DOI: 10.23641/asha.26409088
title: Dual-task interference and listening effort (Ceuleers et al., 2024)

description: <b>Purpose: </b>The purpose of the current study was to assess dual-task interference (i.e., changes between the dual-task and baseline condition) in a listening effort dual-task paradigm in normal-hearing (NH) adults, hearing aid (HA) users, and cochlear implant (CI) users.<b>Method: </b>Three groups of 31 participants were included: (a) NH adults, (b) HA users, and (c) CI users. The dual-task paradigm consisted of a primary speech understanding task in a quiet condition, and a favorable and unfavorable noise condition, and a secondary visual memory task. Dual-task interference was calculated for both tasks, and participants were classified based on their patterns of interference. Descriptive analyses were established and differences between the three groups were examined.<b>Results: </b>The descriptive results showed varying patterns of dual-task interference between the three listening conditions. Most participants showed the pattern of visual memory interference (i.e., worse results for the secondary task in the dual-task condition and no difference for the primary task) in the quiet condition, whereas the pattern of speech understanding priority trade-off (i.e., worse results for the secondary task in the dual-task condition and better results for the primary task) was most prominent in the unfavorable noise condition. Particularly, in HA and CI users, this shift was seen. However, the patterns of dual-task interference were not statistically different between the three groups.<b>Conclusions: </b>Results of this study may provide additional insight into the interpretation of dual-task paradigms for measuring listening effort in diverse participant groups. It highlights the importance of considering both the primary and secondary tasks for accurate interpretation of results.<b>Supplemental Material S1. </b>Exact values of the descriptive data per group (NH, HA users, and CI users) and results for one-way ANOVA or Kruskall–Wallis for the primary task in the baseline- and dual-task condition.<b>Supplemental Material S2.</b> Exact values of the descriptive data per group (NH, HA users, and CI users) and results for one-way ANOVA for the secondary task in the baseline- and dual-task condition.<b>Supplemental Material S3.</b> Exact values of the descriptive data per group (NH, HA users, and CI users) and results for one-way ANOVA or Kruskall–Wallis for the dual-task effect (DTE = 100 × [score in dual-task condition - score in baseline condition]/score in baseline condition).Ceuleers, D., Degeest, S., Swinnen, F., Baudonck, N., Kestens, K., Dhooge, I., & Keppler, H. (2024). Dual-task interference in the assessment of listening effort: Results of normal-hearing adults, cochlear implant users, and hearing aid users<i>. Journal of Speech, Language, and Hearing Research</i>, <i>67</i>(9), 3201-3216. https://doi.org/10.1044/2024_JSLHR-23-00636
DOI: 10.23641/asha.26409088.v1
title: Dual-task interference and listening effort (Ceuleers et al., 2024)

description: <b>Purpose: </b>The purpose of the current study was to assess dual-task interference (i.e., changes between the dual-task and baseline condition) in a listening effort dual-task paradigm in normal-hearing (NH) adults, hearing aid (HA) users, and cochlear implant (CI) users.<b>Method: </b>Three groups of 31 participants were included: (a) NH adults, (b) HA users, and (c) CI users. The dual-task paradigm consisted of a primary speech understanding task in a quiet condition, and a favorable and unfavorable noise condition, and a secondary visual memory task. Dual-task interference was calculated for both tasks, and participants were classified based on their patterns of interference. Descriptive analyses were established and differences between the three groups were examined.<b>Results: </b>The descriptive results showed varying patterns of dual-task interference between the three listening conditions. Most participants showed the pattern of visual memory interference (i.e., worse results for the secondary task in the dual-task condition and no difference for the primary task) in the quiet condition, whereas the pattern of speech understanding priority trade-off (i.e., worse results for the secondary task in the dual-task condition and better results for the primary task) was most prominent in the unfavorable noise condition. Particularly, in HA and CI users, this shift was seen. However, the patterns of dual-task interference were not statistically different between the three groups.<b>Conclusions: </b>Results of this study may provide additional insight into the interpretation of dual-task paradigms for measuring listening effort in diverse participant groups. It highlights the importance of considering both the primary and secondary tasks for accurate interpretation of results.<b>Supplemental Material S1. </b>Exact values of the descriptive data per group (NH, HA users, and CI users) and results for one-way ANOVA or Kruskall–Wallis for the primary task in the baseline- and dual-task condition.<b>Supplemental Material S2.</b> Exact values of the descriptive data per group (NH, HA users, and CI users) and results for one-way ANOVA for the secondary task in the baseline- and dual-task condition.<b>Supplemental Material S3.</b> Exact values of the descriptive data per group (NH, HA users, and CI users) and results for one-way ANOVA or Kruskall–Wallis for the dual-task effect (DTE = 100 × [score in dual-task condition - score in baseline condition]/score in baseline condition).Ceuleers, D., Degeest, S., Swinnen, F., Baudonck, N., Kestens, K., Dhooge, I., & Keppler, H. (2024). Dual-task interference in the assessment of listening effort: Results of normal-hearing adults, cochlear implant users, and hearing aid users<i>. Journal of Speech, Language, and Hearing Research</i>, <i>67</i>(9), 3201-3216. https://doi.org/10.1044/2024_JSLHR-23-00636
DOI: 10.5064/f6ymwpux/8tuptm
title: Harrison_s1_p21_clinician_MD_interview.pdf
DOI: 10.5064/f6ymwpux/4j5j4c
title: Harrison_s3_p12_clinician_SW_interview.pdf
DOI: 10.5064/f6ymwpux/zzjz8h
title: Harrison_s4_p47_leader_RN_interview.pdf
DOI: 10.5064/f6ymwpux/bikcid
title: Harrison_s2_p34_leader_RN_interview.pdf
DOI: 10.5064/f6ymwpux/gesmcz
title: Harrison_s3_p9_clinician_SW_interview.pdf
DOI: 10.5064/f6ymwpux/zl7tjm
title: Harrison_s2_p28_clinician_SW_interview.pdf
DOI: 10.5064/f6ymwpux/6vxcg6
title: Harrison_s1_p22_clinician_RN_interview.pdf
DOI: 10.5064/f6ymwpux/qvunzi
title: Harrison_s1_p23_clinician_sw_interview.pdf
DOI: 10.5064/f6ymwpux/kxmw1s
title: Harrison_s3_p4_leader_interview.pdf
DOI: 10.5064/f6ymwpux/vz0nb1
title: Harrison_s2_p39_clinician_RN_interview.pdf
DOI: 10.5064/f6ymwpux/wzhdwr
title: Harrison_s2_p37_clinician_CP_interview.pdf
DOI: 10.5064/f6ymwpux/vwubgz
title: Harrison_s2_p35_clinician_RN_interview.pdf
DOI: 10.5064/f6ymwpux/plo7ly
title: Harrison_s4_p41_leader_RN_interview.pdf
DOI: 10.5064/f6ymwpux/wwhmgp
title: Harrison_s4_p49_QI_interview.pdf
DOI: 10.5064/f6ymwpux/leftqq
title: Harrison_s4_p46_clinician_SW_interview.pdf
DOI: 10.5064/f6ymwpux/row88c
title: Harrison_s4_p40_clinician_RN_part1 & 2_interview.pdf
DOI: 10.5064/f6ymwpux/ziom1y
title: Harrison_s1_p25_clinician_RN_interview.pdf
DOI: 10.5064/f6ymwpux/x57ju6
title: Harrison_s3_p14_clinician_sw_interview.pdf
DOI: 10.5064/f6ymwpux/0tyiqy
title: Harrison_s2_p36_clinician_RN_interview.pdf
DOI: 10.5064/f6ymwpux/ge5d3i
title: Harrison_s4_p44_clinican_MD_interview.pdf
DOI: 10.5064/f6ymwpux/btt4go
title: Harrison_s2_p30_clinician_QI-RN_notes.pdf
DOI: 10.5064/f6ymwpux/oglktl
title: Harrison_s2_p38_clinician_RN_interview.pdf
DOI: 10.5064/f6ymwpux/rfj0ga
title: Harrison_s3_p8_leader_interview.pdf
DOI: 10.5064/f6ymwpux/ste8sr
title: Harrison_s4_p48_clinician_SW_interview.pdf
DOI: 10.5064/f6ymwpux/sfk5p9
title: Harrison_s2_p27_leader_MD_interview.pdf
DOI: 10.5064/f6ymwpux/a81zzv
title: Harrison_s4_p42_leader_MD_part1 & 2_interview.pdf
DOI: 10.5064/f6ymwpux/rf3px5
title: Harrison_s3_p5_QI_NP_interview.pdf
DOI: 10.5064/f6ymwpux/i8ttuj
title: Harrison_s3_p6_clinician_RN_interview.pdf
DOI: 10.5064/f6ymwpux/vf7sco
title: Harrison_s1_p19_clinician_RN_interview.pdf
DOI: 10.5064/f6ymwpux/kfwsmm
title: Harrison_s2_p32_clinician_MD_interview.pdf
DOI: 10.5064/f6ymwpux/2qvefj
title: Harrison_s1_p18_QI_leader_interview.pdf
DOI: 10.5064/f6ymwpux/vb7ayn
title: Harrison_s3_p2_leader_APN_interview.pdf
DOI: 10.5064/f6ymwpux/xovivz
title: Harrison_s1_leader_p16_interview.pdf
DOI: 10.5064/f6ymwpux/rtak1z
title: Harrison_s4_p51_clinician_SW_part_1 & 2_interview.pdf
DOI: 10.5064/f6ymwpux/kmlgqq
title: Harrison_s1_p20_clinician_SW_interview.pdf
DOI: 10.5064/f6ymwpux/wrrynh
title: Harrison_s2_p31_clinician_SW_RN_interview.pdf
DOI: 10.5064/f6ymwpux/mgnfzb
title: Harrison_s1_p26_clinician_RN_interview.pdf
DOI: 10.5064/f6ymwpux/gztfv6
title: Harrison_s3_p7_clinician_RN_interview.pdf
DOI: 10.5064/f6ymwpux/w0nubw
title: Harrison_Codebook.pdf
DOI: 10.5064/f6ymwpux/jhubf3
title: Harrison_HospiceInformedConsent.pdf
DOI: 10.5064/f6ymwpux/bhw1g3
title: Harrison_ConceptualModel.pdf
DOI: 10.5064/f6ymwpux/kv9rrj
title: Harrison_InterviewGuide_Leaders.pdf
DOI: 10.5064/f6ymwpux/j7v6n1
title: Harrison_COMBMethods.pdf
DOI: 10.5064/f6ymwpux/falvih
title: Harrison_InterviewGuide_Clinicians.pdf
DOI: 10.5064/f6ymwpux/gjqzul
title: Harrison_Glossary.pdf
DOI: 10.5064/f6ymwpux/yjgfn2
title: Harrison_InterviewGuide_QualityImprovementAdministrators.pdf
DOI: 10.5064/f6ymwpux/jmh5vr
title: README_Harrison.txt
DOI: 10.5064/f6ymwpux/fhyawk
title: Harrison_Codebook.pdf
DOI: 10.5064/f6ymwpux/4fl9qk
title: Harrison_COMBMethods.pdf
DOI: 10.5064/f6ymwpux/dxqkuh
title: Harrison_ConceptualModel.pdf
DOI: 10.5064/f6ymwpux/9selqw
title: Harrison_Glossary.pdf
DOI: 10.5064/f6ymwpux/0exfma
title: Harrison_HospiceInformedConsent.pdf
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