The Process, Outcomes, and Challenges of Feasibility Studies Conducted in Partnership With Stakeholders: A Health Intervention for Women Survivors of Intimate Partner Violence
Bibliographic record
Abstract
Feasibility studies play a crucial role in determining whether complex, community-based interventions should be subject to efficacy testing. Reports of such studies often focus on efficacy potential but less often examine other elements of feasibility, such as acceptance by clients and professionals, practicality, and system integration, which are critical to decisions for proceeding with controlled efficacy testing. Although stakeholder partnership in feasibility studies is widely suggested to facilitate the research process, strengthen relevance, and increase knowledge transfer, little is written about how this occurs or its consequences and outcomes. We began to address these gaps in knowledge in a feasibility study of a health intervention for women survivors of intimate partner violence (IPV) conducted in partnership with policy, community and practitioner stakeholders. We employed a mixed-method design, combining a single-group, pre-post intervention study with 52 survivors of IPV, of whom 42 completed data collection, with chart review data and interviews of 18 purposefully sampled participants and all 9 interventionists. We assessed intervention feasibility in terms of acceptability, demand, practicality, implementation, adaptation, integration, and efficacy potential. Our findings demonstrate the scope of knowledge attainable when diverse elements of feasibility are considered, as well as the benefits and challenges of partnership. The implications of diverse perspectives on knowledge transfer are discussed. Our findings show the importance of examining elements of feasibility for complex community-based health interventions as a basis for determining whether controlled intervention efficacy testing is justified and for refining both the intervention and the research design.
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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 | 0.281 | 0.356 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| 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".