Promoting the use of self-management in novice chiropractors treating individuals with spine pain: the design of a theory-based knowledge translation intervention
Bibliographic record
Abstract
BACKGROUND: Clinical practice guidelines generally recommend clinicians use self-management support (SMS) when managing patients with spine pain. However, even within the educational setting, the implementation of SMS remains suboptimal. The objectives of this study were to 1) estimate the organizational readiness for change toward using SMS at the Canadian Memorial Chiropractic College (CMCC), Toronto, Ontario from the perspective of directors and deans, 2) estimate the attitudes and self-reported behaviours towards using evidence-based practice (EBP), and beliefs about pain management among supervisory clinicians and chiropractic interns, 3) identify potential barriers and enablers to using SMS, and 4) design a theory-based tailored Knowledge Translation (KT) intervention to increase the use of SMS. METHODS: Mixed method design. We administered three self-administered questionnaires to assess clinicians' and interns' attitudes and behaviours toward EBP, beliefs about pain management, and practice style. In addition, we conducted 3 focus groups with clinicians and interns based on the Theoretical Domain Framework (TDF) to explore their beliefs about using SMS for patients with spine pain. Data were analysed using deductive thematic analysis by 2 independent assessors. A panel of 7 experts mapped behaviour change techniques to key barriers identified informing the design of a KT intervention. RESULTS: Participants showed high level of EBP knowledge, positive attitude of EBP, and moderate frequency of EBP use. A number of barrier factors were identified from clinicians (N = 6) and interns (N = 16) corresponding to 7 TDF domains: Knowledge; Skills; Environmental context and resources; Emotion; Beliefs about Capabilities; Memory, attention & decision making; and Social Influence. To address these barriers, the expert panel proposed a multifaceted KT intervention composed of a webinar and online educational module on a SMS guided by the Brief Action Planning, clinical vignettes, training workshop, and opinion leader support. CONCLUSION: SMS strategies can help maximizing the health care services for patients with spine pain. This may in turn optimize patients' health. The proposed theory-based KT intervention may facilitate the implementation of SMS among clinicians and interns.
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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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".