Theoretical domains framework to assess barriers to change for planning health care quality interventions: a systematic literature review
Bibliographic record
Abstract
BACKGROUND: Theoretical domains framework (TDF) provides an integrative model for assessing barriers to behavioral changes in order to suggest interventions for improvement in behavior and ultimately outcomes. However, there are other tools that are used to assess barriers. OBJECTIVE: The objective of this study is to determine the degree of concordance between domains and constructs identified in two versions of the TDF including original (2005) and refined version (2012) and independent studies of other tools. METHODS: We searched six databases for articles that studied barriers to health-related behavior changes of health care professionals or the general public. We reviewed quantitative papers published in English which included their questionnaires in the article. A table including the TDF domains of both original and refined versions and related constructs was developed to serve as a reference to describe the barriers assessed in the independent studies; descriptive statistics were used to express the results. RESULTS: Out of 552 papers retrieved, 50 were eligible to review. The barrier domains explored in these articles belonged to two to eleven domains of the refined TDF. Eighteen articles (36%) used constructs outside of the refined version. The spectrum of barrier constructs of the original TDF was broader and could meet the domains studied in 48 studies (96%). Barriers in domains of "environmental context and resources", "beliefs about consequences", and "social influences" were the most frequently explored in 42 (84%), 37 (74%), and 33 (66%) of the 50 articles, respectively. CONCLUSION: Both refined and original TDFs cataloged barriers measured by the other studies that did not use TDF as their framework. However, the original version of TDF explored a broader spectrum of barriers than the refined version. From this perspective, the original version of the TDF seems to be a more comprehensive tool for assessing barriers in practice.
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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 | 0.080 | 0.175 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.014 |
| Bibliometrics | 0.049 | 0.029 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| 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, 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".