Critically appraising and utilising qualitative health research evidence in nutrition practice
Bibliographic record
Abstract
BACKGROUND: Evidence-based decision-making and practice recommendations are commonly based on findings from quantitative studies or reviews. In the present study, we provide an overview of how to incorporate findings from qualitative research into the evidence-based decision-making process. METHODS: To illustrate how qualitative evidence can be integrated into the decision-making process, we have outlined a clinical nutrition scenario and the process for sourcing credible evidence to inform decision-making. A qualitative health research study was selected and appraised using the Critical Appraisal Skill Programme (CASP) appraisal tool for qualitative research. Based on the results of the critical appraisal, the study quality is considered, and we discuss whether the qualitative evidence can be applied to practice. RESULTS: A detailed description of how the qualitative findings can be used conceptually and instrumentally in practice to address the clinical nutrition scenario is provided. CONCLUSIONS: Developing skills in critically appraising findings from qualitative studies will increase awareness and utilisation of this type of evidence in practice and policy, with a goal to ensure that patient/client perceptions are considered, leading to enhanced person-centred care or systems.
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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 | 0.488 | 0.688 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.022 | 0.017 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.008 | 0.008 |
| 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, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".