A qualitative study examining healthcare managers and providers’ perspectives on participating in primary care implementation research
Bibliographic record
Abstract
BACKGROUND: Primary care reforms should be supported by high-quality evidence across the entire life cycle of research. Front-line healthcare providers play an increasing role in implementation research. We recently evaluated two interventions for people with type 2 diabetes (T2D) in partnership with four Primary Care Networks (PCNs) in Alberta, Canada. Here, we report healthcare professionals perspectives on participating in primary care implementation research. METHODS: Guided by the RE-AIM framework, we collected qualitative data before, during, and after both interventions. We conducted 34 in-person or telephone interviews with 17 individual PCN professionals. We used content analysis to identify emerging codes and concepts. RESULTS: Two major themes emerged from the data. First, healthcare managers were eager to conduct implementation research in a primary care setting. Second, regardless of willingness to conduct research, there were challenges to implementing experimental study designs for both interventions. PCN professionals presumed the interventions were better than usual care, expressed role conflict, and reported administrative burdens related to research participation. Perceptions of patient vulnerability and an obligation to intervene exacerbated these issues. CONCLUSIONS: Healthcare professionals with limited practical research experience might not foresee the challenges in implementing experimental study designs in primary care settings to generate high-quality evidence. These issues are intensified when healthcare professionals perceive target patient populations as vulnerable and in need of intervention based on the presenting illness. Possible solutions include further research training, involving healthcare professionals in study design development, and using non-clinical staff to conduct research activities, particularly among acutely unwell patient populations.
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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 | 0.054 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.012 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".