Exploring perceived barriers and enablers to fidelity of training and delivery of an intervention to reduce imaging for low back pain: a qualitative interview study protocol
Bibliographic record
Abstract
Background: Diagnostic imaging has limited utility in the assessment and management of non-specific low back pain (LBP), but remains commonly used in clinical practice. Interventions have been designed to reduce non-indicated imaging for LBP; however, evidence of effectiveness has been variable. It is unclear whether intervention fidelity was adequately assessed in these interventions, which may have an impact on the interpretation of trial results. Within implementation research, intervention fidelity refers to the degree to which an intervention was delivered as intended and to the strategies used to monitor and enhance this process. Intervention fidelity covers five domains: design, training, delivery, receipt, and enactment. Objectives: The objectives of this study are to explore perceived barriers and enablers to fidelity of training and delivery of a proposed theory-informed intervention aimed at reducing non-indicated imaging for LBP by general practitioners (GPs) and chiropractors in Newfoundland and Labrador (NL), Canada. Methods: Semi-structured interviews will be conducted with GPs and chiropractors in NL to explore their views on barriers and enablers towards enhancing and/or assessing fidelity of training and delivery. Interviews will be audio-recorded, transcribed verbatim, and analysed with the Theoretical Domains Framework. Relevant domains related to perceived barriers and enablers will be identified by: the frequency of beliefs; the presence of conflicting beliefs; and the perceived strength of the impact a belief may have on the target behaviours. Discussion: Results of this study will aid in the development of a fidelity protocol for an upcoming cluster randomised controlled trial of a theory-informed intervention aimed at reducing non-indicated imaging for LBP. Our results may help to ensure that the proposed intervention will be delivered with good fidelity and that fidelity can be appropriately assessed.
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 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 | 0.072 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".