Rural interprofessional primary health care team development and sustainability: establishing a research agenda
Bibliographic record
Abstract
Primary health care (PHC) plays a pivotal role in health system reform locally and globally. The use of well functioning interprofessional primary health care (IPHC) teams is recognized as a key strategy in widespread health system reform across global, national, and provincial jurisdictions. IPHC teams contribute to the improvement of the health and well being of the population. These teams engage in issues that are a priority for citizens, such as: providing good evidence-based care; supporting the efforts of individuals, families, and communities in leading healthy lives; actively and deliberatively involving citizens in decisions affecting their health and health care system; and addressing the systemic social, economic, and political causes of health disparities, such as poverty, violence, and rural isolation. Many jurisdictions have begun to experiment with and implement major changes in the delivery of PHC. This has required that health care managers and practitioners reconsider the ways in which they have traditionally worked. However, although many innovative PHC services were developed, the notion of how to best develop and sustain the service delivery team itself and within what contexts could have used more deliberate attention. There are no documented best practices for rural IPHC team development and sustainability in the scholarly literature. This paper presents the results of a literature review, including the empirical and conceptual evidence regarding team development, team sustainability, and the role of rural context in IPHC team development. An argument for advancing PHC research that focuses on rural IPHC team development and sustainability is posited.
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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 | 0.080 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.020 | 0.030 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".