Bibliographic record
Abstract
Mixed methods research is increasingly recognized for its importance in advancing pediatric psychology, allowing investigators to address the complexity of child health and development more comprehensively (Wu et al., 2019). However, integrating qualitative and quantitative data remains a challenge that researchers do not always fully achieve or adequately report (Creswell & Clark, 2017). Knafl et al. (this issue) aimed to address this gap with a pragmatic guide for researchers new to mixed methods, offering step-by-step advice for planning, integrating, and reporting data across three core mixed methods designs. Strengths of this article include its use of examples from pediatric research, concrete recommendations for addressing integration at each stage, and explicit discussion of reporting guidelines. This guide is a valuable resource that helps to make the strengths of mixed methods research more accessible and actionable for researchers in pediatric psychology. There is a clear need for researchers in pediatric psychology to develop competency in mixed methods research approaches (Wu et al., 2019). One domain which is rapidly accelerating and for which mixed methods research is critical is intervention development (Czajkowski et al., 2015). Such research often involves the collection of qualitative and quantitative data, requiring particular attention to how these data are integrated and reported. For example, when designing an intervention following the Obesity-Related Behavioral Intervention Trials (ORBIT) model, different phases may require mixed methods, with findings from earlier phases informing refinements to the intervention in subsequent phases (Czajkowski et al., 2015). Reporting these iterative, multi-phase findings concisely and transparently can be challenging (see Ogez et al., 2021), making the guidance from Knafl et al. (this issue) for data preparation and proactive integration planning especially valuable. When applied in intervention research, mixed methods are essential to generate comprehensive insights for intervention improvement, capture stakeholder needs, and contextualize outcomes (Aschbrenner et al., 2022).
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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 | 0.091 | 0.448 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.019 | 0.008 |
| Research integrity | 0.086 | 0.085 |
| Insufficient payload (model declined to judge) | 0.034 | 0.027 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".