A methodological review protocol of the use of Bayesian factor analysis in primary care research
Bibliographic record
Abstract
BACKGROUND: The development of questionnaires for primary care practice and research is of increasing interest in the literature. In settings where valuable prior knowledge or preliminary data is available, Bayesian factor analysis can be used to incorporate such information when conducting questionnaire construct validation. This protocol outlines a methodological review that will summarize evidence on the current use of Bayesian factor analysis in the primary care literature. METHODS: A comprehensive search strategy has been developed and will be used to identify relevant literature (research studies in primary care) indexed in MEDLINE, Scopus, EMBASE, CINAHL, and Cochrane Library. The search strategy includes terms and synonyms for Bayesian factor analysis and primary care. The reference lists of relevant articles being identified will be screened to find further relevant studies. At least two reviewers will independently extract data and resolve discrepancies through consensus. Descriptive analyses will summarize the use and reporting of Bayesian factor analysis approaches for validating questionnaires applicable to primary care. DISCUSSION: This methodological review will provide a comprehensive overview of the current use and reporting of Bayesian factor analysis in primary care and will provide recommendations for its proper future use. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42018114978.
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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 | 0.274 | 0.315 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.011 | 0.014 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.089 | 0.021 |
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".