Factors influencing clinicians' referral or admission decisions for post‐acute stroke or traumatic brain injury rehabilitation: A scoping review
Bibliographic record
Abstract
Demand for post-acute stroke and traumatic brain injury (TBI) rehabilitation outweighs resource availability. Every day, clinicians face the challenging task of deciding which patient will benefit or not from rehabilitation. The objectives of this scoping review were to map and compare factors reported by clinicians as influencing referral or admission decisions to post-acute rehabilitation for stroke and TBI patients, to identify most frequently reported factors and those perceived as most influential. We searched four major databases for articles published between 1946 and January 2021. Articles were included if they reported clinicians' perceptions, investigated referral or admission decisions to post-acute rehabilitation, and focused on patients with stroke or TBI. Twenty articles met inclusion criteria. The International Classification of Functioning, Disability and Health framework was used to guide data extraction and summarizing. Patient-related factors most frequently reported by clinicians were age, mental status prior to stroke or TBI, and family support. The two latter were ranked among the most influential by clinicians working with stroke patients, whereas age was ranked of low importance. Organizational factors were reported to influence decisions (particularly the availability of post-acute care services) as well as clinicians' characteristics (eg, knowledge). Moreover, clinicians' prediction of patient outcome ranked among the most important driver of referral or admission decisions by clinicians working with stroke patients. Findings highlight the complex nature of decision-making regarding patient selection for rehabilitation and provide insight on important factors that frontline clinicians need to consider when having to make rapid decisions in high-pressured acute care environments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".