Implementation of the Good Life with osteoArthritis in Denmark (GLA:D <sup>®</sup> ) program via telehealth in Australia: A mixed-methods program evaluation
Bibliographic record
Abstract
Introduction We aimed to evaluate the implementation of the Good Life with osteoArthritis in Denmark (GLA:D ® ) program via telehealth in Australia using Reach, Effectiveness, Adoption, Implementation, and Maintenance Qualitative Evaluation for Systematic Translation framework. Methods Using a convergent mixed-methods design, semi-structured one-on-one interviews with physiotherapist adopters and nonadopters of GLA:D ® via telehealth were analyzed thematically alongside the examination of registry data (1 March 2020–10 February 2022) from patients with hip or knee osteoarthritis completing GLA:D ® via telehealth (telehealth-only) or combined with in-person care (hybrid). Effectiveness was determined as changes from baseline to 3-month follow-up (mean differences, 95% confidence intervals, effect size) for Knee injury and Osteoarthritis Outcome Score (KOOS-12)/Hip disability and Osteoarthritis Outcome Score-12 (HOOS-12), and chair stand test. Group- and individual-level changes were compared to published minimally clinically important change scores. Results Twenty-three interviews (12 adopters, 11 nonadopters) found key barriers/facilitators to reach and adoption, high perceived effectiveness, and strategies to support sustainability. Of 2612 registered patients, 85 (3%) and 115 (4%) completed GLA:D ® via telehealth-only or hybrid model, respectively. Most effectiveness outcomes were associated with moderate-large improvements. Group-level changes exceeded minimally clinically important change values for KOOS/HOOS-quality of life and chair stand test. Nearly two out of three patients reached a minimally clinically important change for KOOS/HOOS-quality of life. With telehealth-only and hybrid delivery, 99% ( n = 82) and 85% ( n = 97) were satisfied/very satisfied. Physiotherapist adoption was limited ( n = 128, 6%). Discussion GLA:D ® delivered via telehealth is effective, had high patient satisfaction, and was perceived positively by physiotherapist adopters. Addressing low reach and adoption requires further implementation strategies to facilitate greater telehealth opportunities for patients and physiotherapists.
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 | 0.036 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".