Physician Enabling Skills Questionnaire Validation d’un outil récemment développé en contexte de soins primaires
Bibliographic record
Abstract
OBJECTIVE: To evaluate the reliability and validity of the newly developed Physician Enabling Skills Questionnaire (PESQ) by assessing its internal consistency, test-retest reliability, concurrent validity with patient-centred care, and predictive validity with patient activation and patient enablement. DESIGN: Validation study. SETTING: Saguenay, Que. PARTICIPANTS: One hundred patients with at least 1 chronic disease who presented in a waiting room of a regional health centre family medicine unit. MAIN OUTCOME MEASURES: Family physicians’ enabling skills, measured with the PESQ at 2 points in time (ie, while in the waiting room at the family medicine unit and 2 weeks later through a mail survey); patient-centred care, assessed with the Patient Perception of Patient-Centredness instrument; patient activation, assessed with the Patient Activation Measure; and patient enablement, assessed with the Patient Enablement Instrument. RESULTS: The internal consistency of the 6 subscales of the PESQ was adequate (Cronbach α = .69 to .92). The test-retest reliability was very good (r = 0.90; 95% CI 0.84 to 0.93). Concurrent validity with the Patient Perception of Patient-Centredness instrument was good (r = −0.67; 95% CI −0.78 to −0.53; P < .001). The PESQ accounts for 11% of the total variance with the Patient Activation Measure (r(2) = 0.11; P = .002) and 19% of the variance with the Patient Enablement Instrument (r(2) = 0.19; P < .001). CONCLUSION: The newly developed PESQ presents good psychometric properties, allowing for its use in practice and research.
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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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".