Use of mixed methods research in intervention studies to increase young people’s interest in STEM: A systematic methodological review
Bibliographic record
Abstract
Introduction: Mixed methods research intervention studies integrate quantitative evaluation approaches, such as randomized controlled trials and quasi-experimental designs, with qualitative research to evaluate the effectiveness, efficacy, or other results of an intervention or program. These types of studies, which have attracted growing attention in recent years, enhance the scope and rigor of the evaluation. While various frameworks that summarize the justifications for carrying out these types of studies and provide implementation guidance have been published in the last few years in the health sciences, we do not know whether such frameworks have been properly implemented in the social and educational sciences. This review examined the methodological features and reporting practices of mixed methods intervention studies aimed at increasing young people's interest in STEM. Methods: A systematic search was carried out in APA PsycNET, ERIC, ProQuest, Scopus, and Web of Science, and a hand search in 20 journals. We included peer-reviewed English-language articles that reported intervention studies with a quantitative component measuring outcomes specific to increasing secondary school students' interest in STEM fields, a qualitative component conducted before, during, or after the quantitative component, and evidence of integration of both components. Qualitative content analysis and ideal-type analysis were used to synthesize the findings. Results: We found 34 studies; the majority published in the last ten years. Several patterns of mixed methods application were described in these studies, illustrating the unique insights that can be gained by employing this methodology. The reporting quality of the included studies was generally adequate, especially regarding the justification for using a mixed methods intervention design and the integration of the quantitative and qualitative components. Nonetheless, a few reporting issues were observed, such as a lack of detail in the presentation of the mixed methods design, an inadequate description of the qualitative sampling and analysis techniques, and the absence of joint displays for representing integration. Discussion: Authors must pay attention to these issues to ensure that the insights obtained by the use of mixed methods research are effectively communicated.
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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: Methods · 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 | Systematic review | high |
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 | 0.270 | 0.451 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.016 | 0.018 |
| Bibliometrics | 0.051 | 0.040 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".