Quality of Intervention Research Reporting in Medical Rehabilitation Journals
Bibliographic record
Abstract
OBJECTIVE: To evaluate the degree to which rehabilitation researchers report information on the interventions they evaluate. DESIGN: Intervention research articles published in six United States medical rehabilitation journals in 1997-1998 were rated on the presence or absence of information on the overall design, intervention used, and outcome measures. Rating was performed independently by two authors who used discussion to resolve disagreements. RESULTS: A total of 171 articles were identified. The use of randomization was not reported in 5% of articles, the nature of data collection was absent in 6%, and the timing of the intervention relative to the onset of the disorder was absent in 32%. For 73% of 651 outcome measures used in the articles, no clinimetric information was reported. Descriptions of the 344 interventions used were inadequate or absent in 62% of the articles and lacked an operational definition in 9%. Intervention integrity was assessed for only 46% of the articles. No journal was systematically better or worse than average. CONCLUSIONS: There is a need for rehabilitation researchers to improve the quality of their research and the quality of research reporting. Suggestions for doing so are made.
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 | 0.680 | 0.925 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.050 | 0.053 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.004 | 0.002 |
| 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; the direct Gemma label and the distilled Codex classifier 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".