Practice-based collection of quality indicator data for a comprehensive quality assessment programme in Canadian family practices
Bibliographic record
Abstract
INTRODUCTION: Quality improvement in primary care can be facilitated by the ability to measure indicators in practice. This paper reports on the process and impacts of data collection on indicators of a quality assessment tool in seven interprofessional group family practices in Ontario, Canada. METHODS: The programme addressed indicators and collected data across multiple domains of practice including clinical quality, physical factors, and patient and staff perceptions. A system audit of the practice, a patient survey, a staff satisfaction survey and chart audits (on hypothyroidism and hyperlipidaemia) were designed to measure selected indicators across the domains. Practices were trained and collected their own data. Practices provided feedback on the process and impacts during a postprogramme workshop and on a survey 1 year later. RESULTS: Four-hundred charts audits were completed for each of hyperlipidaemia and hypothyroidism, 319 patient satisfaction surveys were administered in four practices, and the staff satisfaction survey was completed by 77 staff in six practices. Most practices demonstrated indicators of privacy, access and safety. There was more variability in indicators relating to staff professional development and team involvement in meetings. Patient satisfaction with providers was rated highly, whereas some aspects of practice access were rated lower. Practices approached the challenge of participation by engaging multidisciplinary team members and dividing tasks. Most practices reported continued participation in various quality improvement initiatives 1 year later. CONCLUSIONS: Using a set of indicators, structured processes and training, family practices find the process of gathering and reviewing their data useful for quality improvement.
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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 | 0.043 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".