Barriers and facilitators to reducing overuse of thyroid function testing: a mixed-methods study
Bibliographic record
Abstract
BACKGROUND: Thyroid function laboratory testing is often overused. Tailored de-implementation interventions require an understanding of underlying barriers and facilitators contributing to overuse. METHODS: We performed a mixed-methods study exploring barriers and facilitators of appropriate thyroid function testing using surveys and focus groups conducted between June and October 2023 in British Columbia, Canada. Quantitative survey data were summarised using simple statistics, and open-ended survey questions were summarised using summative content analysis. Focus group transcripts were analysed using thematic analysis. Key themes were mapped onto the combined Theoretical Domains Framework and Capability, Opportunity, Motivation-Behaviour model. RESULTS: 230 practitioners completed the survey (1.4% response rate), and 53 practitioners attended a total of six focus groups. Three themes emerged around barriers from synthesising the results: patient expectations, practitioner knowledge gaps and health system factors. Patient expectations were linked to non-specific symptoms, recommendations from alternate care providers, increased interest in hormone testing and internet searches, leading to patient requests for more testing and/or referrals to specialists. Knowledge gaps included use of specialised tests, interpretation of free hormone results, frequency of thyroid testing and screening in asymptomatic, pregnant and postpartum patients. Health system barriers included lack of practitioner time, lack of family doctors leading more patients to seek care from alternative providers, existing order sets and ordering processes, and existing culture of ordering practices. Identified facilitators of behaviour change towards appropriate thyroid testing included educational resources for practitioners and patients, leveraging of health information systems for seamless viewing of prior test results, reflexive testing and provision of personalised practitioner feedback. CONCLUSIONS: Interventions to reduce overutilisation of thyroid testing should include easily accessible physician educational and feedback resources, patient educational materials and changes to laboratory ordering processes and information systems. Future studies should develop and evaluate the use of these intervention elements in British Columbia.
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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 | 0.021 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".