Experiences of using the Theoretical Domains Framework across diverse clinical environments: a qualitative study
Bibliographic record
Abstract
BACKGROUND: The Theoretical Domains Framework (TDF) is an integrative framework developed from a synthesis of psychological theories as a vehicle to help apply theoretical approaches to interventions aimed at behavior change. PURPOSE: This study explores experiences of TDF use by professionals from multiple disciplines across diverse clinical settings. METHODS: Mixed methods were used to examine experiences, attitudes, and perspectives of health professionals in using the TDF in health care implementation projects. Individual interviews were conducted with ten health care professionals from six disciplines who used the TDF in implementation projects. Deductive content and thematic analysis were used. RESULTS: Three main themes and associated subthemes were identified including: 1) reasons for use of the TDF (increased confidence, broader perspective, and theoretical underpinnings); 2) challenges using the TDF (time and resources, operationalization of the TDF) and; 3) future use of the TDF. CONCLUSION: The TDF provided a useful, flexible framework for a diverse group of health professionals working across different clinical settings for the assessment of barriers and targeting resources to influence behavior change for implementation projects. The development of practical tools and training or support is likely to aid the utility of TDF.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.029 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.013 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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, 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".