Practical mixed methods strategies used to integrate qualitative and quantitative methods in community-based primary health care research
Bibliographic record
Abstract
BACKGROUND: Mixed methods (MM) are common in community-based primary health care (CBPHC) research studies. Several strategies have been proposed to integrate qualitative and quantitative components in MM, but they are seldom well conceptualized and described. The purpose of the present review was to identify and describe practical MM strategies and combinations of strategies used to integrate qualitative and quantitative methods in CBPHC research. METHODS: A methodological review with qualitative synthesis (grouping) was performed. Records published in English in 2015 were retrieved from the Scopus bibliographic database. Eligibility criteria were: CBPHC empirical study, MM research with detailed description of qualitative and quantitative methods and their integration. Data were extracted from included studies and grouped using a conceptual framework comprised of three theoretical types of MM integration, the seven combinations of these types and nine practical strategies (three per type of integration) and multiple combinations of strategies. RESULTS: Among the 151 articles reporting CBPHC and MM studies retrieved, 54 (35.7%) met the inclusion criteria for this review. The included studies provided examples of the three theoretical types of MM integration, the seven combinations of these types as well as the nine practical strategies. Overall, 15 combinations of these strategies were observed. No emerging strategy was observed that was not predicted by the conceptual framework. CONCLUSIONS: This review can provide guidance to CBPHC researchers for planning, conducting and reporting practical strategies and combinations of strategies used for integrating qualitative and quantitative methods in MM research.
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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 | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Metaresearch Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | medium |
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 | 0.619 | 0.639 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.037 | 0.038 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.010 | 0.019 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".