Transforming community-based primary health care delivery through comprehensive performance measurement and reporting: examining the influence of context
Bibliographic record
Abstract
Abstract Background Community-based primary health care represents various community-based health care (CBPHC) models that incorporate health promotion and community development to deliver first-contact health services. Learning health systems (LHSs) are essential for improving CBPHC in which feedback from relevant stakeholders is used to continuously improve health systems with the goal of achieving population health and health equity. Performance reporting is one way to present data to clinicians and decision makers to facilitate a process of reflection, participation, and collaboration among partners to improve CBPHC. Methods Our objective was to obtain feedback on a regional CBPHC performance portrait through key informant interviews. We used purposive convenience sampling to recruit participants who were clinicians in primary care and/or decision-makers in primary care at a regional level. The performance portrait summarized results of survey questions asked of patients, providers, and primary care organizations. The portrait was organized by the 10 pillars of the Patient’s Medical Home (PMH) model. Interview questions specifically asked about portrait content, formatting, interpretability, utility, and dissemination strategies. Content analysis was used to analyze interview data. Results We completed 19 interviews with key informants from the Canadian provinces of Nova Scotia (n = 8), Ontario (n = 6) and British Columbia (n = 5). We coded transcripts into four content areas: (1) Usability as influenced by content and interpretability, (2) Formatting, (3) Utility, and (4) Dissemination. Using data and reporting back to clinicians and decision-makers about how their practices and jurisdictions are performing in primary care in meaningful ways is important. Our results suggest having available methodology notes, including the analysis used to develop any scoring, sampling and sample sizes, and interpretation of the statistics is necessary. Conclusions This research was the first to create a comprehensive performance portrait using data driven by factors that are important to primary care partners. We obtained important feedback on the portrait in the context of usability, formatting, utility, and dissemination. This data needs to be used to provide feedback in continuous cycles to evaluate and improve CBPHC models as part of a LHS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.067 | 0.122 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 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".