Barriers to following imaging guidelines for the treatment and management of patients with low-back pain in primary care: a qualitative assessment guided by the Theoretical Domains Framework
Bibliographic record
Abstract
Abstract Background Low back pain (LBP) is a leading cause of disability and is among the top five reasons that patients visit their family doctors. Over-imaging for non-specific low back pain remains a problem in primary care. To inform a larger study to develop and evaluate a theory-based intervention to reduce inappropriate imaging, we completed an assessment of the barriers and facilitators to reducing unnecessary imaging for NSLBP among family doctors in Newfoundland and Labrador (NL). Methods This was an exploratory, qualitative study describing family doctors’ experiences and practices related to diagnostic imaging for non-specific LBP in NL, guided by the Theoretical Domains Framework (TDF). Data were collected using in-depth, semi-structured interviews. Transcripts were analyzed deductively (assigning text to one or more domains) and inductively (generating themes at each of the domains) before the results were examined to determine which domains should be targeted to reduce imaging. Results Nine family doctors (four males; five females) working in community (n = 4) and academic (n = 5) clinics in both rural (n = 6) and urban (n = 3) settings participated in this study. We found five barriers to reducing imaging for patients with NSLBP: 1) negative consequences, 2) patient demand 3) health system organization, 4) time, and 5) access to resources. These were related to the following domains: 1) beliefs about consequences, 2) beliefs about capabilities, 3) emotion, 4) reinforcement, 5) environmental context and resources, 6) social influences, and 7) behavioural regulation. Conclusions Family physicians a) fear that if they do not image they may miss something serious, b) face significant patient demand for imaging, c) are working in a system that encourages unnecessary imaging, d) don’t have enough time to counsel patients about why they don’t need imaging, and e) lack access to appropriate practitioners, community programs, and treatment modalities to prescribe to their patients. These barriers were related to seven TDF domains. Successfully reducing inappropriate imaging requires a comprehensive intervention that addresses these barriers using established behaviour change techniques. These techniques should be matched directly to relevant TDF domains. The results of our study represent the important first step of this process – identifying the contextual barriers and the domains to which they are related.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".