Opportunities and challenges around adapting supported employment interventions for people with chronic low back pain: modified nominal group technique
Bibliographic record
Abstract
PURPOSE: To identify and rank opportunities and challenges around adapting supported employment interventions for people with chronic low back pain (LBP). METHODS: Delegates from an international back and neck research forum were invited to join an expert panel. A modified nominal group technique (NGT) was used with four stages: silent generation, round robin, clarification, and ranking. Ranked items were reported back and ratified by the panel. RESULTS: Nine experienced researchers working in the fields related to LBP and disability joined the panel. Forty-eight items were generated and grouped into 12 categories of opportunities/challenges. Categories ranked most important related respectively to policy and legislation, ensuring operational integration across different systems, funding interventions, and managing attitudes towards work and health, workplace flexibility, availability of "good" work for this client group, dissonance between client and system aims, timing of interventions, and intervention development. CONCLUSIONS: An expert panel believes the most important opportunities/challenges around adapting supporting employment interventions for people with chronic LBP are facilitating integration/communication between systems and institutions providing intervention components, optimising research outputs for informing policy needs, and encouraging discussion around funding mechanisms for research and interventions. Addressing these factors may help improve the quality and impact of future interventions.Implications for rehabilitationInteraction pathways between health, employment, and social systems need to be improved to effectively deliver intervention components that necessarily span these systems.Research-policy communication needs to be improved by researchers and policy makers, so that research outputs can be consumed by policy makers, and so that researchers recognise the gaps in knowledge needed to underpin policy.Improvements in research-policy communication and coordination would facilitate the delivery of research output at a time when it is likely to make the most impact on policy-making.Discussion and clarification surrounding funding mechanisms for research and interventions may facilitate innovation generally.
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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 | 0.072 | 0.149 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".