Application of the theoretical framework of acceptability in a surgical setting: Theoretical and methodological insights
Bibliographic record
Abstract
PURPOSE: Methods for assessing acceptability of healthcare interventions have been inconsistent until the development of the theoretical framework of acceptability (TFA). Despite its rapid adoption in healthcare research, the TFA has rarely been used to assess acceptability of surgical interventions. We sought to explore the sufficiency of the TFA in this context and provide methodological guidance to support systematic use of this framework in research. METHOD: Acceptability was assessed in a consecutive sample of 15 patients at least 3 months post-joint replacement surgery via theory-informed semi-structured interviews. A detailed description of the application of the TFA is reported. This includes: development of the interview guide (including questions to assess theoretical sufficiency), analysis of interview data and interpretation of findings. RESULTS: Interview data were substantially codable into the TFA constructs but required the addition of a construct, labelled 'perceived safety and risk', and relabelling and redefining an existing construct (new label: 'opportunity costs and gains'). Methodological recommendations for theory-informed interview studies include producing interview support material to enhance precision of the intervention description, conducting background conversations with a range of stakeholders in the healthcare setting, and conducting first inductive and then deductive thematic analysis. CONCLUSION: The sufficiency of the TFA could be enhanced for use when assessing interventions with an identifiable risk profile, such as surgery, by the inclusion of an additional construct to capture perceptions of risk and safety. We offer these methodological recommendations to guide researchers and facilitate consistency in the application of the TFA in theory-informed interview studies.
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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 |
|---|---|---|---|
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | high |
| opus | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Qualitative | 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 | 0.236 | 0.216 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.006 | 0.051 |
| Scholarly communication | 0.013 | 0.020 |
| Open science | 0.006 | 0.017 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".