Physician-reported barriers to using evidence-based recommendations for low back pain in clinical practice: a systematic review and synthesis of qualitative studies using the Theoretical Domains Framework
Bibliographic record
Abstract
BACKGROUND: Adoption of low back pain guidelines is a well-documented problem. Information to guide the development of behaviour change interventions is needed. The review is the first to synthesise the evidence regarding physicians' barriers to providing evidence-based care for LBP using the Theoretical Domains Framework (TDF). Using the TDF allowed us to map specific physician-reported barriers to individual guideline recommendations. Therefore, the results can provide direction to future interventions to increase physician compliance with evidence-based care for LBP. METHODS: We searched the literature for qualitative studies from inception to July 2018. Two authors independently screened titles, abstracts, and full texts for eligibility and extracted data on study characteristics, reporting quality, and methodological rigour. Guided by a TDF coding manual, two reviewers independently coded the individual study themes using NVivo. After coding, we assessed confidence in the findings using the GRADE-CERQual approach. RESULTS: Fourteen studies (n = 318 physicians) from 9 countries reported barriers to adopting one of the 5 guideline-recommended behaviours regarding in-clinic diagnostic assessments (9 studies, n = 198), advice on activity (7 studies, n = 194), medication prescription (2 studies, n = 39), imaging referrals (11 studies, n = 270), and treatment/specialist referrals (8 studies, n = 193). Imaging behaviour is influenced by (1) social influence-from patients requesting an image or wanting a diagnosis (n = 252, 9 studies), (2) beliefs about consequence-physicians believe that providing a scan will reassure patients (n = 175, 6 studies), and (3) environmental context and resources-physicians report a lack of time to have a conversation with patients about diagnosis and why a scan is not needed (n = 179, 6 studies). Referrals to conservative care is influenced by environmental context and resources-long wait-times or a complete lack of access to adjunct services prevented physicians from referring to these services (n = 82, 5 studies). CONCLUSIONS: Physicians face numerous barriers to providing evidence-based LBP care which we have mapped onto 7 TDF domains. Two to five TDF domains are involved in determining physician behaviour, confirming the complexity of this problem. This is important as interventions often target a single domain where multiple domains are involved. Interventions designed to address all the domains involved while considering context-specific factors may prove most successful in increasing guideline adoption. REGISTRATION: PROSPERO 2017, CRD42017070703.
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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 | 0.196 | 0.342 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.019 | 0.018 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".