Exploring ways to replace low-value care with high-value care in physiotherapy
Bibliographic record
Abstract
Despite an increase in the use of physiotherapy services in Australia, the need to replace low-value care with high-value care in physiotherapy has received little-to-no attention. The overall aim of this thesis is to explore strategies to replace low-value care with high-value care in physiotherapy. Chapter Two explores the importance of the Choosing Wisely campaign in addressing low-value care in musculoskeletal health and the evidence behind the success of the campaign in medicine. Chapter Three explores physiotherapists’ attitudes, views, and beliefs towards the Australian Physiotherapy Association’s (APA) Choosing Wisely recommendations and the barriers and facilitators to physiotherapists using them. Chapter Four investigates whether language influences physiotherapists’ willingness to follow the APA Choosing Wisely recommendations. Chapter Five investigates the effectiveness of Choosing Wisely recommendations in reducing physiotherapists’ intentions to refer for imaging and use electrotherapy for low back pain compared to no recommendations. Chapter Six presents the findings of a systematic review that determined the proportion of clinicians that are aware of five imaging decision rules (Ottawa Ankle Rules, Ottawa Knee Rule, Canadian C-Spine Rule, National Emergency X-Radiography Utilization Study (NEXUS) and Canadian Computed Tomography Head Rules) and the proportion that use them in practice. Our results suggested low awareness and use of these five imaging decision rules. Chapter Seven presents the findings of a systematic review that evaluated the effectiveness of various implementation strategies for increasing clinicians’ use of the five validated imaging decision rules mentioned in Chapter Six. This thesis has filled important gaps in evidence and has the potential to make a positive shift from low-value to high-value care in the field of physiotherapy and also holds promise for improving healthcare practices more broadly.
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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 | 0.052 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".