Evaluation of strategies used to promote the GLA:D® Back program was possible.
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Evaluation of health care processes can inform implementation of future models.
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The GLA:D® Back program was well adopted, mostly by physiotherapists.
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GLA:D® Back care in chiropractic setting were mostly ran by physiotherapists.
Abstract
Background
Translation of research into practice is a methodological challenge. The GLA:D® Back program was initiated to implement evidence-based care for people with low back pain inspired by GLA:D® (Good Life with osteoArthritis in Denmark) that has succeeded in implementing evidence-based care for knee and hip osteoarthritis. This study evaluates the spread and reach of promotion initiatives for GLA:D® Back clinician courses, and the adoption of the GLA:D® Back intervention in clinical practice.
Methods
Pre-defined success criteria addressed; i) spread; achievement of intended promotion activities (e.g. social media), ii) reach; recruitment of clinicians with certain profiles (e.g. gender balance). Adoption was defined as patient enrollment in the GLA:D® Back registry by course participants. Univariate and multivariate logistic regression was used to investigate associations between adoption and clinician characteristics.
Results
Most clinicians signed up based on information from colleagues (22%). Pre-defined goals for reach, except one, was obtained. 23% (140) of clinicians initiated the GLA:D® Back program in clinical practice within <90 days of course participation; mainly physiotherapists (p < 0.001). The odds ratio for starting GLA:D® Back patient care in a chiropractic setting was 7.4 [2.5; 21.4], indicating that physiotherapists employed by chiropractors mostly handled the intervention.
Conclusion
Future promotion strategies should recognize the influence of colleagues and professional networks. Converting clinician courses into patient care was mostly adopted physiotherapists. Although, evaluation processes were less useful in this study, future evaluation of health care processes has potential to inform the implementation of new models in future studies.
