047 Identifying Factors Predictive of Managing Patients with Low Back Pain without Using X-Rays Among North American Chiropractors: Applying Psychological Theories to Evidence-Based Clinical Practice
Bibliographic record
Abstract
Background This study aimed to identify theoretically based modifiable factors that predict whether chiropractors manage patients with low back pain without ordering lumbar x-rays. Methods A mailed survey with psychological measures was collected from a random sample of Ontario (Canada) and Practice Network (US) chiropractors. The outcome measures were behavioural intention and behavioural simulation (scenario decision-making). Explanatory variables included constructs from motivational theories (Theory of Planned Behaviour (TPB), Theory of Interpersonal Behaviour (TIB)), action theories (Operant Learning Theory (OLT) and Planning (action and coping)), and two other constructs: personal moral norm and habit as measured by the Self-Reported Habit Index (SRHI). Multiple regression analyses examined the predictive value of each theoretical model individually for simulation and intention outcomes. Results 31% of North American chiropractors returned completed questionnaires. Overall, TPB and TIB, followed by personal moral norms and OLT best explained behavioural simulation. Theory level variance explained among Ontario and US chiropractors was respectively: TPB 59%; 52.0%, TIB 57%; 54.0%, personal moral norm 49%; 46.0%, OLT 49%; 52.0%, action planning 28%; 29%, and SRHI 42%; 48%. Constructs from TPB and TIB best explained behavioural intention. Theory level variance explained was respectively: TPB 85%; 74%, TIB; 83%; 81%, OLT 62%; 69%, and SRHI 59% and 52% for SRHI. Conclusion These models explained up to 59% of the variation in behavioural simulation and up to 85% in intention to manage back pain patients without x-rays. Results may inform development of theory-based behaviour change interventions to implement imaging guideline recommendations among North American chiropractors. These models explained up to 59% of the variation in behavioural simulation and up to 85% in intention to manage back pain patients without x-rays. Results may inform development of theory-based behaviour change interventions to implement imaging guideline recommendations among North American chiropractors.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | medium |
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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".