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Record W4281658624 · doi:10.1186/s12875-022-01751-6

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

2022· article· en· W4281658624 on OpenAlexafffundabout
Andrea Pike, Andrea M. Patey, Rebecca Lawrence, Kris Aubrey‐Bassler, Jeremy Grimshaw, Sameh Mortazhejri, Shawn Dowling, Yamile Jasaui, Sacha Bhatia, D’Arcy Duquettes, Erin Gionet, Kyle R. Kirkham, Wendy Levinson, Brian T. Johnston, Kelly Mrklas, Patrick S. Parfrey, Justin Presseau, Todd Sikorski, Monica Taljaard, Kednapa Thavorn, Krista Mahoney, Shannon M. Ruzycki, Amanda Häll

Bibliographic record

VenueBMC Primary Care · 2022
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsUniversity of CalgaryJaneway Children's Health and Rehabilitation CentreHealth Sciences CentreMemorial University of Newfoundland
FundersCanadian Institutes of Health Research
KeywordsContext (archaeology)Qualitative researchLow back painIntervention (counseling)Exploratory researchMedicineFamily medicinePrimary carePsychologyNursingAlternative medicinePathologyGeographySociology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.023
metaresearch head score (Gemma)0.032
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.124

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.032
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0070.007
Scholarly communication0.0030.002
Open science0.0020.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.012
GPT teacher head0.335
Teacher spread0.324 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations30
Published2022
Admission routes3
Has abstractyes

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