What systemic factors contribute to collaboration between primary care and public health sectors? An interpretive descriptive study
Bibliographic record
Abstract
BACKGROUND: Purposefully building stronger collaborations between primary care (PC) and public health (PH) is one approach to strengthening primary health care. The purpose of this paper is to report: 1) what systemic factors influence collaborations between PC and PH; and 2) how systemic factors interact and could influence collaboration. METHODS: This interpretive descriptive study used purposive and snowball sampling to recruit and conduct interviews with PC and PH key informants in British Columbia (n = 20), Ontario (n = 19), and Nova Scotia (n = 21), Canada. Other participants (n = 14) were knowledgeable about collaborations and were located in various Canadian provinces or working at a national level. Data were organized into codes and thematic analysis was completed using NVivo. The frequency of "sources" (individual transcripts), "references" (quotes), and matrix queries were used to identify potential relationships between factors. RESULTS: We conducted a total of 70 in-depth interviews with 74 participants working in either PC (n = 33) or PH (n = 32), both PC and PH (n = 7), or neither sector (n = 2). Participant roles included direct service providers (n = 17), senior program managers (n = 14), executive officers (n = 11), and middle managers (n = 10). Seven systemic factors for collaboration were identified: 1) health service structures that promote collaboration; 2) funding models and financial incentives supporting collaboration; 3) governmental and regulatory policies and mandates for collaboration; 4) power relations; 5) harmonized information and communication infrastructure; 6) targeted professional education; and 7) formal systems leaders as collaborative champions. CONCLUSIONS: Most themes were discussed with equal frequency between PC and PH. An assessment of the system level context (i.e., provincial and regional organization and funding of PC and PH, history of government in successful implementation of health care reform, etc) along with these seven system level factors could assist other jurisdictions in moving towards increased PC and PH collaboration. There was some variation in the importance of the themes across provinces. British Columbia participants more frequently discussed system structures that could promote collaboration, power relations, harmonized information and communication structures, formal systems leaders as collaboration champions and targeted professional education. Ontario participants most frequently discussed governmental and regulatory policies and mandates for collaboration.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".