Contextual factors influencing the implementation of innovations in community-based primary health care: the experience of 12 Canadian research teams
Bibliographic record
Abstract
The objectives of this paper are to: (1) identify contextual factors such as policy that impacted the implementation of community-based primary health care (CBPHC) innovations among 12 Canadian research teams and (2) describe strategies used by the teams to address contextual factors influencing implementation of CBPHC innovations. In primary care settings, consideration of contextual factors when implementing change has been recognized as critically important to success. However, contextual factors are rarely recorded, analyzed or considered when implementing change. The lack of consideration of contextual factors has negative implications not only for successfully implementing primary health care (PHC) innovations, but also for their sustainability and scalability. For this evaluation, data collection was conducted using self-administered questionnaires and follow-up telephone interviews with team representatives. We used a combination of directed and conventional content analysis approaches to analyze the questionnaire and interview data. Representatives from all 12 teams completed the questionnaire and 11 teams participated in the interviews; 40 individuals participated in this evaluation. Four themes representing contextual factors that impacted the implementation of CBPHC innovations were identified: (I) diversity of jurisdictions (II) complexity of interactions and collaborations (III) policy, and (IV) the multifaceted nature of PHC. The teams used six strategies to address these contextual factors including: (1) conduct an environmental scan at the beginning (2) maintaining engagement among partners and stakeholders by encouraging open and inclusive communication; (3) contextualizing the innovation for different settings; (4) anticipating and addressing changes, delays, and the need for additional resources; (5) fostering a culture of research and innovation among partners and stakeholders; and (6) ensuring information about the innovation is widely available. Implementing CBPHC innovations across jurisdictions is complex and involves navigating through multiple contextual factors. Awareness of the dynamic nature of context should be considered when implementing innovations.
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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 | 0.029 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.041 | 0.012 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.004 | 0.004 |
| 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".