Intervention fidelity within interventions aimed at reducing non-indicated imaging for low back pain
Bibliographic record
Abstract
Non-indicated imaging for low back pain (LBP) is unnecessary but remains common. Interventions to reduce this behaviour must consider the impact of fidelity (i.e., degree to which the intervention was delivered as intended) on trial results. The thesis examines strategies used to enhance and assess intervention fidelity for interventions targeting nonindicated imaging for LBP and explores perceived barriers and enablers to enhancing fidelity of training and delivery to a proposed intervention for reducing non-indicated imaging for LBP. Two studies, a systematic review and a qualitative interview study, address these objectives. The systematic review, conducted using the PRISMA statement, found few studies reported strategies to enhance/assess fidelity. When reported, mainly enhancement strategies for fidelity to study design and intervention delivery were identified. The interview study, analysed with the Theoretical Domains Framework, found that logistical issues were a perceived barrier to attending training, while enablers were incentives and flexibility in training. Time, patient pressures, and habit were perceived barriers to intervention delivery, while enablers included enhancement strategies related to reminders and support. Findings from this thesis contribute to the development of an intervention fidelity protocol when designing an intervention to reduce non-indicated imaging for LBP in Newfoundland and Labrador, Canada.
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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 | 0.158 | 0.387 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".