Expediting evidence synthesis for healthcare decision-making: exploring attitudes and perceptions towards rapid reviews using Q methodology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Rapid reviews expedite the knowledge synthesis process with the goal of providing timely information to healthcare decision-makers who want to use evidence-informed policy and practice approaches. A range of opinions and viewpoints on rapid reviews is thought to exist; however, no research to date has formally captured these views. This paper aims to explore evidence producer and knowledge user attitudes and perceptions towards rapid reviews. METHODS: A Q methodology study was conducted to identify central viewpoints about rapid reviews based on a broad topic discourse. Participants rank-ordered 50 text statements and explained their Q-sort in free-text comments. Individual Q-sorts were analysed using Q-Assessor (statistical method: factor analysis with varimax rotation). Factors, or salient viewpoints on rapid reviews, were identified, interpreted and described. RESULTS: Analysis of the 11 individual Q sorts identified three prominent viewpoints: Factor A cautions against the use of study design labels to make judgements. Factor B maintains that rapid reviews should be the exception and not the rule. Factor C focuses on the practical needs of the end-user over the review process. CONCLUSION: Results show that there are opposing viewpoints on rapid reviews, yet some unity exists. The three factors described offer insight into how and why various stakeholders act as they do and what issues may need to be resolved before increase uptake of the evidence from rapid reviews can be realized in healthcare decision-making environments.
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.
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 | 0.024 | 0.234 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it