Using implementation science to build intimate partner violence screening and referral capacity in a fracture clinic
Bibliographic record
Abstract
RATIONALE, AIMS, AND OBJECTIVES: Over the past two decades, research informing good clinical practices related to intimate partner violence (IPV) has been plentiful, yet partner violence screening remains challenging to translate into action. In spite of the documented efficacy of routine screening for women of reproductive age and the availability of validated screening instruments, many IPV screening programmes lack the components necessary for success. In Toronto, a multidisciplinary team of researchers and clinicians is using the tools of implementation science to scale up an evidence-based IPV screening and response programme in an urban orthopaedic clinic where prior screening attempts have been ineffective. METHODS: Using the Active Implementation Framework as a guide, researchers collected data across multiple sources to inform the first stage of implementation. Analysis focused on identifying the characteristics of the clinic that support or hinder implementation of new processes, evidence-based screening practices that fit with the clinic, and characteristics of a strong implementation team. RESULTS: Through this process, researchers and clinicians uncovered organizational strengths and weaknesses related to IPV screening that may not have been identified previously. The need to incorporate technology into our screening processes became apparent, as did the importance of shared communication and colearning between clinicians and researchers. CONCLUSIONS: The benefits of investing in the preparatory phases of implementation are discussed. Without undertaking the process of gathering and analysing data, examining the factors that support effective and sustainable implementation, and investing in the creation of a strong implementation team, it is likely that decisions about our screening approaches would have resulted in a less-effective and sustainable process.
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 | 0.226 | 0.290 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.009 | 0.021 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".