Exploring the implementation of an evidence-based health promotion intervention for women experiencing intimate partner violence (iHEAL) in diverse contexts: Study Protocol
Bibliographic record
Abstract
OBJECTIVES: This participatory, mixed methods study will explore how iHEAL, a woman-led, nurse-delivered health promotion intervention for women who have experienced intimate partner violence (IPV), can be implemented in real-world, community-based health care settings located in 3 Canadian provinces. Grounded in the Active Implementation Frameworks, the study's primary aim is to identify the processes, resources and supports necessary to implement and sustain this novel program with fidelity while maintaining its benefits for women. METHODS/DESIGN: Over 2.5 years, each organization will plan for and deliver the iHEAL program, supported by an iHEAL Consultant. To explore implementation processes and fidelity, successes and challenges, and any value-added of iHEAL to organizations and/or communities, qualitative interviews will be conducted with 3 groups of participants: 1) organizational leaders; 2) implementation/delivery team members (nurses and supervisors); and 3) external stakeholders or agencies supporting iHEAL through referrals or other collaboration. High level notes capturing key issues and decisions at planning meetings will supplement these data. Administrative program data will be collected to assess program reach, participant engagement, and aspects of fidelity. Women participating in iHEAL will also be invited to complete pre/post intervention surveys to assess changes in key outcomes, with a subsample of 60 women to be interviewed about their experiences of iHEAL and suggestions for strengthening the program. Qualitative data will be analyzed using Rapid Team Based Qualitative Analysis and Reflective Thematic Analysis. Quantitative data will be summarized using descriptive statistics; pre-post intervention changes in outcomes collected in women's surveys will be analyzed using paired t-tests. Ethical approval has been obtained, and all participants will provide informed consent. SIGNIFICANCE: The findings of this research are expected to yield insights about organizational factors that shape the delivery of iHEAL and support the development of guidance materials for future iHEAL implementation and scale up.
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 | 0.052 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.030 | 0.004 |
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".