Informing the implementation and use of person-centred quality indicators: a mixed methods study on the readiness, barriers and facilitators to implementation in Canada
Bibliographic record
Abstract
OBJECTIVES: To ensure optimal implementation of person-centred quality indicators (PC-QIs), we assessed the readiness of Canadian healthcare organisations and explored their perceived barriers and facilitators to implementing and using PC-QIs. DESIGN: Mixed methods. SETTING AND PARTICIPANTS: Representatives of Canadian healthcare delivery and coordinating organisations that guide the development and/or implementation of person-centred care (PCC) measurement. Representatives from primary care clinics and organisations from the province of Alberta, Canada also participated. METHODS: We conducted a survey with representatives of Canadian healthcare organisations. The survey comprised two sections that: (1) assessed readiness for using PC-QIs, and (2) were based on the Organizational Readiness for Change Assessment tool. We summarised the survey results using descriptive statistics. We then conducted follow-up interviews with organisations representing system and clinical-level perspectives to further explore barriers and facilitators to implementing PC-QIs. The interviews were informed by and analysed using the Consolidated Framework for Implementation Research. RESULTS: Thirty-three Canadian regional healthcare organisations across all 13 provinces/territories participated in the survey. Only 5 of 26 PC-QIs were considered highly feasible to implement for 75% of organisations and included: coordination of care, communication, structures to report performance, engaging patients and caregivers and overall experience. A representative sample of 10 system-level organisations and 11 primary care organisations/clinics participated in the interviews. Key barriers identified were: resources and staff capacity for quality improvement, a shift in focus to COVID-19 and health provider motivation. Facilitators included: prioritisation of PCC measurement, leadership and champion engagement, alignment with ongoing provincial strategic direction and measurement efforts, and the use of technology for data collection, management and reporting. CONCLUSIONS: Despite high interest and policy alignment to use PC-QI 'readiness' to implement them effectively remains a challenge. Organisations need to be supported to collect, use and report PCC data to make the needed improvements that matter to patients.
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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 | 0.033 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".