Implementation interventions for musculoskeletal programs of care in the active military and barriers, facilitators, and outcomes of implementation: a scoping review
Bibliographic record
Abstract
BACKGROUND: Musculoskeletal disorders are common in the active military and are associated with significant lost duty days and disability. Implementing programs of care to manage musculoskeletal disorders can be challenging in complex healthcare systems such as in the military. Understanding how programs of care for musculoskeletal disorders have been implemented in the military and how they impact outcomes may help to inform future implementation interventions in this population. METHODS: We conducted a scoping review using the modified Arksey and O'Malley framework to identify literature on (1) implementation interventions of musculoskeletal programs of care in the active military, (2) barriers and facilitators of implementation, and (3) implementation outcomes. We identified studies published in English by searching MEDLINE, CINAHL, Embase, and CENTRAL (Cochrane) from inception to 1 June 2018 and hand searched reference lists of relevant studies. We included empirical studies. We synthesized study results according to three taxonomies: the Effective Practice and Organization of Care (EPOC) taxonomy to classify the implementation interventions; the capability, opportunity, motivation-behavior (COM-B) system to classify barriers and facilitators of implementation; and Proctor et al.'s taxonomy (Adm Policy Ment Health 38:65-76, 2011) to classify outcomes in implementation research. RESULTS: We identified 1785 studies and 16 were relevant. All but two of the relevant studies were conducted in the USA. Implementation interventions were primarily associated with delivery arrangements (e.g., multidisciplinary care). Most barriers or facilitators of implementation were environmental (physical or social). Service and client outcomes indicated improved efficiency of clinical care and improved function and symptomology. Studies reporting implementation outcomes indicated the programs were acceptable, appropriate, feasible, or sustainable. CONCLUSION: Identification of evidence-based approaches for the management of musculoskeletal disorders is a priority for active-duty military. Our findings can be used by military health services to inform implementation strategies for musculoskeletal programs of care. Further research is needed to better understand (1) the components of implementation interventions, (2) how to overcome barriers to implementation, and (3) how to measure implementation outcomes to improve quality of care and recovery from musculoskeletal disorders.
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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 | 0.038 | 0.157 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.017 | 0.017 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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, 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".