Qualitative Research and Its Importance in Adapting Interventions
Bibliographic record
Abstract
Systematic approaches are essential when adapting interventions, so the adapted intervention is feasible, acceptable, and holds promise for positive outcomes in the new target population and/or setting. Qualitative research is critical to this process. The purpose of this article is to provide an example of how qualitative research was used to guide the adaptation a web-based intervention for family carers of persons with dementia residing in long-term care (LTC) and to discuss challenges associated with using qualitative methodologies in this regard. Four steps are outlined: (a) choosing an intervention to adapt, (b) validating the conceptual framework of the intervention, (c) revising the intervention, and (d) conducting a feasibility study. Challenges with respect to decontextualization and subjective reality are discussed, with suggestions provided on how to overcome them. The result of this process was a feasible and acceptable web-based intervention to support family carers of persons with dementia residing in LTC.
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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 | 0.636 | 0.606 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.018 | 0.073 |
| Scholarly communication | 0.026 | 0.029 |
| Open science | 0.007 | 0.024 |
| Research integrity | 0.011 | 0.016 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".