Barriers and facilitators to development and implementation of a rural primary health care intervention for dementia: a process evaluation
Bibliographic record
Abstract
Abstract Background With rural population aging there are growing numbers of people with dementia in rural and remote settings. The role of primary health care (PHC) is critical in rural locations, yet there is a lack of rural-specific PHC models for dementia, and little is known about factors influencing the development, implementation, and sustainability of rural PHC interventions. Using a community-based participatory research approach, researchers collaborated with a rural PHC team to co-design and implement an evidence-based interdisciplinary rural PHC memory clinic in the Canadian province of Saskatchewan. This paper reports barriers and facilitators to developing, implementing, and sustaining the intervention. Methods A qualitative longitudinal process evaluation was conducted over two and half years, from pre- to post-implementation. Data collection and analyses were guided by the Consolidated Framework for Implementation Research (CFIR) which consists of 38 constructs within five domains: innovation characteristics, outer setting, inner setting, individual characteristics, and process. Data were collected via focus groups with the PHC team and stakeholders, smaller team workgroup meetings, and team member interviews. Analysis was conducted using a deductive approach to apply CFIR codes to the data and an inductive analysis to identify barriers and facilitators. Results Across all domains, 14 constructs influenced development and implementation. Three domains (innovation characteristics, inner setting, process) were most important. Facilitators were the relative advantage of the intervention, ability to trial on a small scale, tension for change, leadership engagement, availability of resources, education and support from researchers, increased self-efficacy, and engagement of champions. Barriers included the complexity of multiple intervention components, required practice changes, lack of formal incentive programs, time intensiveness of modifying the EMR during iterative development, lack of EMR access by all team members, lack of co-location of team members, workload and busy clinical schedules, inability to justify a designated dementia care manager role, and turnover of PHC team members. Conclusions The study identified key factors that supported and hindered the development and implementation of a rural-specific strategy for dementia assessment and management in PHC. Despite challenges related to the rural context, the researcher-academic partnership was successful in developing and implementing the intervention.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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".