What role does compassion have on quality care ratings? A regression analysis and validation of the SCQ in emergency department patients
Bibliographic record
Abstract
OBJECTIVE: To examine the unique contribution of patient reported experiences of compassion to overall patient quality care ratings. Additionally, we assess whether patients' reported experiences of compassion in the emergency department differed between sociodemographic groups. METHODS: Provincial data for this cross-sectional study were collected from 03/01/2022 to 09/05/2022 from 14 emergency departments in Alberta, Canada. Data from 4501 emergency department patients (53.6% women, 77.1% White/European) were analyzed. The primary outcome was patients' overall quality care ratings during their most recent ED visit. Measures included in the hierarchical stepwise regression included demographics, and those drawn from the Emergency Department Patient Experience of Care (EDPEC) questionnaire: single and multi-item measures of patient information (e.g., patient perceptions health) and patient experience (e.g., physician communication), and compassion (e.g., Sinclair Compassion Questionnaire; SCQ-ED). RESULTS: =.23), explaining 19% unique variance beyond all other measures. One-way ANOVAs indicated significant demographic differences in mean compassion scores, such that women (vs. men) reported lower compassion (MD=-.15, 95% CI=-.21, -.09, p<.001), and Indigenous (vs. White) patients reported lower compassion (MD=-.17, 95% CI =-.34, -.01, p=.03). CONCLUSIONS: Compassion was identified as a key contributor to ED overall quality care ratings, and experiences of compassion varied as a function of demographics. Patient-reported compassion is an indicator of quality care that needs to be formally integrated into clinical care and quality care assessments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".