Contextualized analysis of a needs assessment using the Theoretical Domains Framework: a case example in endocrinology
Bibliographic record
Abstract
BACKGROUND: The Theoretical Domains Framework (TDF) is a set of 14 domains of behavior change that provide a framework for the critical issues and factors influencing optimal knowledge translation. Considering that a previous study has identified optimal knowledge translation techniques for each TDF domain, it was hypothesized that the TDF could be used to contextualize and interpret findings from a behavioral and educational needs assessment. To illustrate this hypothesis, findings and recommendations drawn from a 2012 national behavioral and educational needs assessment conducted with healthcare providers who treat and manage Growth and Growth Hormone Disorders, will be discussed using the TDF. METHODS: This needs assessment utilized a mixed-methods research approach that included a combination of: [a] data sources (Endocrinologists (n:120), Pediatric Endocrinologists (n:53), Pediatricians (n:52)), [b] data collection methods (focus groups, interviews, online survey), [c] analysis methodologies (qualitative - analyzed through thematic analysis, quantitative - analyzed using frequencies, cross-tabulations, and gap analysis). Triangulation was used to generate trustworthy findings on the clinical practice gaps of endocrinologists, pediatric endocrinologists, and general pediatricians in their provision of care to adult patients with adult growth hormone deficiency or acromegaly, or children/teenagers with pediatric growth disorders. The identified gaps were then broken into key underlying determinants, categorized according to the TDF domains, and linked to optimal behavioral change techniques. RESULTS: The needs assessment identified 13 gaps, each with one or more underlying determinant(s). Overall, these determinants were mapped to 9 of the 14 TDF domains. The Beliefs about Consequences domain was identified as a contributing determinant to 7 of the 13 challenges. Five of the gaps could be related to the Skills domain, while three were linked to the Knowledge domain. CONCLUSIONS: The TDF categorization of the needs assessment findings allowed recommendation of appropriate behavior change techniques for each underlying determinant, and facilitated communication and understanding of the identified issues to a broader audience. This approach provides a means for health education researchers to categorize gaps and challenges identified through educational needs assessments, and facilitates the application of these findings by educators and knowledge translators, by linking the gaps to recommended behavioral change techniques.
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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 | 0.040 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| 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".