Patient and provider perspectives regarding criteria for patient prioritization in two specialized rehabilitation programs
Bibliographic record
Abstract
To increase fairness and equity in access to rehabilitation services, a strategy emerging from the literature is patient prioritization. Selecting explicit prioritization criteria is a complex task because it is important to simultaneously consider the objectives of all stakeholders. The of this study was to compare service users’ and service providers’ perspectives regarding patient prioritization criteria in two rehabilitation programs. We conducted a multiple case study in two rehabilitation programs, i.e., a driving evaluation program and a compression garment manufacturing program. We sent a web-based survey asking two groups (patients and providers) to individually produce a set of criteria, then individual answers were coded and combined in a single set of criteria. Stakeholders identified a total of 32 criteria to prioritize patients. Some criteria, such as age, occupation, functional level, pain, absence of caregiver, and time since referral, were considered important by both stakeholders in both programs. Patients and providers tended to have similar opinions about criteria to prioritize patients in waitlists. Taking into consideration the opinions of all stakeholders concerning prioritization criteria is an important part of the decision-making process. Experience Framework This article is associated with the Quality & Clinical Excellence lens of The Beryl Institute Experience Framework (https://www.theberylinstitute.org/ExperienceFramework). Access other PXJ articles related to this lens. Access other resources related to this lens.
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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 | 0.027 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".