Understanding how context and culture in six communities can shape implementation of a complex intervention: a comparative case study
Bibliographic record
Abstract
BACKGROUND: Contextual factors can act as barriers or facilitators to scaling-up health care interventions, but there is limited understanding of how context and local culture can lead to differences in implementation of complex interventions with multiple stakeholder groups. This study aimed to explore and describe the nature of and differences between communities implementing Health TAPESTRY, a complex primary care intervention aiming to keep older adults healthier in their homes for longer, as it was scaled beyond its initial effectiveness trial. METHODS: We conducted a comparative case study with six communities in Ontario, Canada implementing Health TAPESTRY. We focused on differences between three key elements: interprofessional primary care teams, volunteer program coordination, and the client experience. Sources of data included semi-structured focus groups and interviews. Data were analyzed through the steps of thematic analysis. We then created matrices in NVivo by splitting the qualitative data by community and comparing across the key elements of the Health TAPESTRY intervention. RESULTS: Overall 135 people participated (39 clients, 8 clinical managers, 59 health providers, 6 volunteer coordinators, and 23 volunteers). The six communities had differences in size and composition of both their primary care practices and communities, and how the volunteer program and Health TAPESTRY were implemented. Distinctions between communities relating to the work of the interprofessional teams included characteristics of the huddle lead, involvement of physicians and the volunteer coordinator, and clarity of providers' role with Health TAPESTRY. Key differences between communities relating to volunteer program coordination included the relationship between the volunteers and primary care practices, volunteer coordinator characteristics, volunteer training, and connections with the community. Differences regarding the client experience between communities included differing approaches used in implementation, such as recruitment methods. CONCLUSIONS: Although all six communities had the same key program elements, implementation differed community-by-community. Key aspects that seemed to lead to differences across categories included the size and spread of communities, size of primary care practices, and linkages between program elements. We suggest future programs engaging stakeholders from the beginning and provide clear roles; target the most appropriate clients; and consider the size of communities and practices in implementation. TRIAL REGISTRATION: ClinicalTrials.gov: NCT03397836 .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.022 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| 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".