"My approach to this job is...one person at a time": Perceived discordance between population-level quality targets and patient-centred care.
Bibliographic record
Abstract
OBJECTIVE: To understand the usefulness of audit and feedback among family physicians and examine the barriers to using it to improve quality of care. DESIGN: Qualitative study using in-depth interviews. SETTING: Family physicians across Ontario participating in audit and feedback initiatives describing the proportion of patients meeting quality targets for chronic disease. PARTICIPANTS: Purposive sampling was conducted to ensure variation in sex, years of experience, and baseline performance for quality metrics. All participants used electronic medical records and worked in multidisciplinary primary care practices. METHODS: Semistructured interviews were conducted with family physicians. The interview guide and initial coding framework were adjusted iteratively in keeping with the constant comparative method. Sampling continued until saturation was reached. Interviews were analyzed using the framework approach. MAIN FINDINGS: Participants reported that the feedback increased their awareness of gaps between ideal and actual performance. This resulted mainly in efforts to "try harder" patient by patient. Key barriers to acting upon feedback in a systematic manner included a perceived discordance between population-level quality targets and patient-centred care, as well as competing priorities at both the patient and organizational levels. Although all participants had electronic medical records, participants reported a lack of quality improvement infrastructure in their practices. CONCLUSION: Family physicians were not highly motivated to achieve evidence-based population-level quality targets for diabetes; many competing organizational and clinical goals took priority. Additional human resources might be needed to translate data in feedback reports into systematic changes that could lead to sustained improvements in quality of care.
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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 | 0.048 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.016 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".