Strategies to reduce waiting times in outpatient rehabilitation services for adults with physical disabilities: A systematic literature review
Bibliographic record
Abstract
OBJECTIVE: Identifying effective strategies to reduce waiting times is a crucial issue in many areas of health services. Long waiting times for rehabilitation services have been associated with numerous adverse effects in people with disabilities. The main objective of this study was to conduct a systematic literature review to assess the effectiveness of service redesign strategies to reduce waiting times in outpatient rehabilitation services for adults with physical disabilities. METHODS: We conducted a systematic review, searching three databases (MEDLINE, CINAHL and EMBASE) from their inception until May 2021. We identified studies with comparative data evaluating the effect of rehabilitation services redesign strategies on reducing waiting times. The Mixed Methods Appraisal Tool was used to assess the methodological quality of the studies. A narrative synthesis was conducted. RESULTS: = 3 low); common flaws being missing information on the pre-redesign setting and characteristics of the populations. Seven articles assessed access processes or referral management strategies (e.g. self-referral), four focused on extending/modifying the roles of service providers (e.g. to triage) and eight changed the model of care delivery (e.g. mode of intervention). The different redesign strategies had positive effects on waiting times in outpatient rehabilitation services. CONCLUSIONS: This review highlights the positive effects of many service redesign strategies. These findings suggest that there are several effective strategies to choose from to reduce waiting times and help better respond to the needs of persons experiencing physical disabilities.
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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 | 0.014 | 0.067 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".