A systematic review of quality of reporting in registered intimate partner violence studies: where can we improve?
Bibliographic record
Abstract
BACKGROUND: Reporting quality is paramount when presenting clinical findings in published research to ensure that we have the highest quality of evidence. Poorly reported clinical findings can result in a number of potential pitfalls, including confusion of the methodology used or selective reporting of study results. There are guidelines and checklists that aim to standardize the way in which studies are reported in the literature to ensure transparency. The use of these reporting guidelines may aid in the appropriate reporting of research, which is of increased importance in highly complex fields like intimate partner violence (IPV). The primary objective of this systematic review is to assess the reporting quality of published IPV studies using the CONSORT and STROBE checklists. METHODS: We performed a systematic review of three large study registries for IPV studies. Of the completed studies, we sought full text publications and used reporting checklists to assess the quality of reporting. RESULTS: Of the 42 randomized controlled trials, the mean score on the CONSORT checklist was 63.5% (23.5/37 items, SD 4.7 items). There were also 12 pilot trials in this systematic review, which scored a mean of 49.3% (19.7/40 items; SD 3.3 items) on the CONSORT extension for pilot trials. We included 12 observational studies which scored a mean of 56.1% (18.5/33 items; SD: 4.1 items). CONCLUSIONS: We identified an opportunity to improve reporting quality by encouraging adherence to reporting guidelines. There should be a particular focus on ensuring that pilot studies report pilot-specific items. All researchers have a responsibility to ensure commitment to high quality reporting to ensure transparency in IPV studies.
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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 | 0.062 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".