Searching for rigour in the reporting of mixed methods population health research: a methodological review
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
The environments in which population health interventions occur shape both their implementation and outcomes. Hence, when evaluating these interventions, we must explore both intervention content and context. Mixed methods (integrating quantitative and qualitative methods) provide this opportunity. However, although criteria exist for establishing rigour in quantitative and qualitative research, there is poor consensus regarding rigour in mixed methods. Using the empirical example of school-based obesity interventions, this methodological review examined how mixed methods have been used and reported, and how rigour has been addressed. Twenty-three peer-reviewed mixed methods studies were identified through a systematic search of five databases and appraised using the guidelines for Good Reporting of a Mixed Methods Study. In general, more detailed description of data collection and analysis, integration, inferences and justifying the use of mixed methods is needed. Additionally, improved reporting of methodological rigour is required. This review calls for increased discussion of practical techniques for establishing rigour in mixed methods research, beyond those for quantitative and qualitative criteria individually. A guide for reporting mixed methods research in population health should be developed to improve the reporting quality of mixed methods studies. Through improved reporting, mixed methods can provide strong evidence to inform policy and practice.
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.
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Reporting · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | medium |
| gpt | Metaresearch Domain: Reporting · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.763 | 0.357 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.007 |
| 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 it