Strengthening primary health care through primary care and public health collaboration: the influence of intrapersonal and interpersonal factors
Bibliographic record
Abstract
AimThe aim of this paper is to examine Canadian key informants' perceptions of intrapersonal (within an individual) and interpersonal (among individuals) factors that influence successful primary care and public health collaboration. BACKGROUND: Primary health care systems can be strengthened by building stronger collaborations between primary care and public health. Although there is literature that explores interpersonal factors that can influence successful inter-organizational collaborations, a few of them have specifically explored primary care and public health collaboration. Furthermore, no papers were found that considered factors at the intrapersonal level. This paper aims to explore these gaps in a Canadian context. METHODS: This interpretative descriptive study involved key informants (service providers, managers, directors, and policy makers) who participated in one h telephone interviews to explore their perceptions of influences on successful primary care and public health collaboration. Transcripts were analyzed using NVivo 9.FindingsA total of 74 participants [from the provinces of British Columbia (n=20); Ontario (n=19); Nova Scotia (n=21), and representatives from other provinces or national organizations (n=14)] participated. Five interpersonal factors were found that influenced public health and primary care collaborations including: (1) trusting and inclusive relationships; (2) shared values, beliefs and attitudes; (3) role clarity; (4) effective communication; and (5) decision processes. There were two influencing factors found at the intrapersonal level: (1) personal qualities, skills and knowledge; and (2) personal values, beliefs, and attitudes. A few differences were found across the three core provinces involved. There were several complex interactions identified among all inter and intra personal influencing factors: One key factor - effective communication - interacted with all of them. Results support and extend our understanding of what influences successful primary care and public health collaboration at these levels and are important considerations in building and sustaining primary care and public health collaborations.
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".