Quality of Reporting Using Good Reporting of A Mixed Methods Study Criteria in Chiropractic Mixed Methods Research: A Methodological Review
Bibliographic record
Abstract
OBJECTIVE: The purpose of this review was to examine the reporting in chiropractic mixed methods research using Good Reporting of A Mixed Methods Study (GRAMMS) criteria. METHODS: In this methodological review, we searched MEDLINE, Embase, CINAHL, and the Index to Chiropractic Literature from the inception of each database to December 31, 2020, for chiropractic studies reporting the use of both qualitative and quantitative methods or mixed qualitative methods. Pairs of reviewers independently screened titles, abstracts, and full-text studies, extracted data, and appraised reporting using the GRAMMS criteria and risk of bias with the Mixed Methods Appraisal Tool (MMAT). Generalized estimating equations were used to explore factors associated with reporting using GRAMMS criteria. RESULTS: Of 1040 citations, 55 studies were eligible for review. Thirty-seven of these 55 articles employed either a multistage or convergent mixed methods design, and, on average, 3 of 6 GRAMMS items were reported among included studies. We found a strong positive correlation in scores between the GRAMMS and MMAT instruments (r = 0.78; 95% CI, 0.66-0.87). In our adjusted analysis, publications in journals indexed in Web of Science (adjusted odds ratio = 2.71; 95% CI, 1.48-4.95) were associated with higher reporting using GRAMMS criteria. Three of the 55 studies fully adhered to all 6 GRAMMS criteria, 4 studies adhered to 5 criteria, 10 studies adhered to 4 criteria, and the remaining 38 adhered to 3 criteria or fewer. CONCLUSION: Our findings suggest that reporting in chiropractic mixed methods research using GRAMMS criteria was poor, particularly among studies with a higher risk of bias.
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How this classification was reachedexpand
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 | high |
| gpt | Metaresearch Domain: Reporting · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.650 | 0.841 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.013 | 0.017 |
| Bibliometrics | 0.033 | 0.037 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.020 | 0.017 |
| Open science | 0.010 | 0.013 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".