When is a research question not a research question?
Bibliographic record
Abstract
BACKGROUND: Research is undertaken to answer important questions yet often the question is poorly expressed and lacks information on the population, the exposure or intervention, the comparison, and the outcome. An optimal research question sets out what the investigator wants to know, not what the investigator might do, nor what the results of the study might ultimately contribute. OBJECTIVE: The purpose of this paper is to estimate the extent to which rehabilitation scientists optimally define their research questions. METHODS: A cross-sectional survey of the rehabilitation research articles published during 2008. Two raters independently rated each question according to pre-specified criteria; a third rater adjudicated all discrepant ratings. RESULTS: The proportion of the 258 articles with a question formulated as methods or expected contribution and not as what knowledge was being sought was 65%; 30% of questions required reworking. The designs which most often had poorly formulated research questions were randomized trials, cross-sectional and measurement studies. CONCLUSION: Formulating the research question is not purely a semantic concern. When the question is poorly formulated, the design, analysis, sample size calculations, and presentation of results may not be optimal. The gap between research and clinical practice could be bridged by a clear, complete, and informative research question.
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 | 0.578 | 0.768 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.006 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.006 | 0.037 |
| Scholarly communication | 0.020 | 0.025 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.027 | 0.018 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".