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| from matplotlib.cm import get_cmap | |
| import plotly.graph_objects as go | |
| import hydralit_components as hc | |
| about_blurb = ''' | |
| ### About the QuAL Score | |
| The Quality of Assessment for Learning score (QuAL score), | |
| was created to evaluate short qualitative comments that are related to specific | |
| scores entered into a workplace-based assessment, | |
| common within the competency-based medical education (CBME) context. | |
| It is rated on a scale of 0-5, with 0 signifying very low quality and 5 very high quality. | |
| It consists of three subscores which are summed to calculate the overall QuAL score: | |
| 1. Evidence - Does the rater provide sufficient evidence about resident performance? (0-no comment at all, 1-no, but comment present, 2-somewhat, 3-yes/full description) | |
| 2. Suggestion - Does the rater provide a suggestion for improvement? (0-no/1-yes) | |
| 3. Connection - Is the rater's suggestion linked to the behavior described? (0-no/1-yes) | |
| The QuAL score has validity evidence for accurately measuring the quality of evaluation comments in CBME. | |
| For more information, see the paper [here](https://doi.org/10.1080/10401334.2019.1708365). | |
| ### About this Tool | |
| The QuAL score accurately rates the quality of narrative comments in CBME, but | |
| it still requires time-consuming manual rating. With large volumes of text generated in a | |
| typical CBME program, large-scale assessment of comment quality is impractical. | |
| This tool uses machine learning (ML) and natural langugage processing (NLP) to automate | |
| the rating of the QuAL score on narratie comments. | |
| We trained a machine learning model to predict each of the three subscores described above. | |
| The resulting models are accurate: | |
| 1. Evidence - Balanced accuracy of 61.5% for a 0-3 result, within-one accuracy of 96.4% | |
| 2. Suggestion - Accuracy of 85%, sensitivity for lack of suggestion 86.2% | |
| 3. Connection - Accuracy of 82%, sensitivity for lack of connection 90% | |
| The models are highly accurate, but not perfect! You may experience times where | |
| the results are not consistent with your interpretation of the text. If you do, please | |
| leave us [feedback](https://forms.gle/PfXxcGmvLYvd9jWz5). This tool is intendened as a demonstration only | |
| and should not be used for high-stakes assessment (yet!). | |
| ''' | |
| class NQDOverview(object): | |
| def __init__(self, parent, results, | |
| dial_cmap='RdYlGn'): | |
| self.p = parent | |
| self.results = results | |
| self.cmap = get_cmap(dial_cmap) | |
| def _get_color(self): | |
| lab = self.results['qual']['label'] | |
| if lab == 0: | |
| color = '#ffffff' | |
| elif lab == 1: | |
| color = '#dc3545' | |
| elif lab == 2: | |
| color = '#f60' | |
| elif lab == 3: | |
| color = '#ffc107' | |
| elif lab == 4: | |
| color = '#6ea728' | |
| elif lab == 5: | |
| color = '#28a745' | |
| # color = self.cmap(self.results['qual']['label'] / 6.0) | |
| # color = f'rgba({int(color[0]*256)}, {int(color[1]*256)}, {int(color[2]*256)}, {int(color[3]*256)})' | |
| return color | |
| def _build_figure(self): | |
| fig = go.Figure(go.Indicator( | |
| mode = "number+gauge", value = self.results['qual']['label'], | |
| domain = {'x': [0.1, 1], 'y': [0, 1]}, | |
| title = {'text' :"<b>QuAL:</b>"}, | |
| gauge = { | |
| 'shape': "bullet", | |
| 'axis': {'range': [-0.5, 5.5]}, | |
| 'steps': [ | |
| {'range': [-0.5, 0.5], 'color': "maroon"}, | |
| {'range': [0.5, 1.5], 'color': 'indianred'}, | |
| {'range': [1.5, 2.5], 'color': "orange"}, | |
| {"range": [2.5, 3.5], 'color': 'gold'}, | |
| {'range': [3.5,4.5], 'color': 'lightgreen'}, | |
| {'range': [4.5,5.5], 'color': 'green'} | |
| ], | |
| 'bar': { | |
| 'color': 'rgba(123, 123, 123, 0.85)', | |
| 'thickness': 0.7 | |
| }})) | |
| fig.update_layout(margin=go.Margin(t=25, b=20), height=125) | |
| return fig | |
| def draw(self): | |
| st = self.p | |
| with st.expander('About the QuAL Score and this Tool', expanded=False): | |
| st.markdown(about_blurb) | |
| fig = self._build_figure() | |
| st.plotly_chart(fig, use_container_width=True) | |
| cols = st.columns(3) | |
| with cols[0]: | |
| q1lab = self.results['q1']['label'] | |
| if q1lab == 0: | |
| md_str = '😥 None' | |
| elif q1lab == 1: | |
| md_str = '😐 Low' | |
| elif q1lab == 2: | |
| md_str = '😊 Medium' | |
| elif q1lab == 3: | |
| md_str = '😁 High' | |
| # prog_score, prog_theme = self.get_prog_setup('q1') | |
| # hc.info_card(title='Level of Detail', content=md_str, sentiment='good', bar_value=prog_score) | |
| st.metric('Level of Detail', md_str, | |
| help='Q1 - Evidence - Does the rater provide sufficient evidence about resident performance? (0-no comment at all, 1-no, but comment present, 2-somewhat, 3-yes/full description)') | |
| prog_score, prog_theme = self.get_prog_setup('q1') | |
| # hc.progress_bar(prog_score, f'{prog_score:.2f}% confident', override_theme=prog_theme) | |
| with cols[1]: | |
| q2lab = self.results['q2i']['label'] | |
| if q2lab == 0: | |
| md_str = '✅ Yes' | |
| else: | |
| md_str = '❌ No' | |
| st.metric('Suggestion Given', (md_str), | |
| help='Q2 - Suggestion - Does the rater provide a suggestion for improvement? (0-no/1-yes)') | |
| prog_score, prog_theme = self.get_prog_setup('q2i') | |
| # hc.progress_bar(prog_score, f'{prog_score:.2f}% confident', override_theme=prog_theme) | |
| with cols[2]: | |
| q3lab = self.results['q3i']['label'] | |
| if q3lab == 0: | |
| md_str = '✅ Yes' | |
| else: | |
| md_str = '❌ No' | |
| st.metric('Suggestion Linked', md_str, | |
| help='Q3 - Connection - Is the rater’s suggestion linked to the behavior described? (0-no/1-yes)') | |
| prog_score, prog_theme = self.get_prog_setup('q3i') | |
| # hc.progress_bar(prog_score, f'{prog_score:.2f}% confident', override_theme=prog_theme) | |
| def get_prog_setup(self, q): | |
| prog_score = self.results[q]['scores'][self.results[q]['label']] * 100 | |
| if prog_score > 75: | |
| prog_sent = '#28a745' | |
| elif (prog_score > 25) and (prog_score <= 75): | |
| prog_sent = '#ffc107' | |
| else: | |
| prog_sent = '#dc3545' | |
| prog_theme = {'content_color': 'white', 'progress_color': '#aaa'} | |
| return prog_score, prog_theme |