Admission to and Continuation of Inpatient Stroke Rehabilitation in Queensland, Australia: A Survey of Factors that Contribute to the Consultant's Decision
Bibliographic record
Abstract
Aim: To evaluate factors that may contribute to the decision of the consultant medical officer (CMO) to: (1) admit a person with stroke to inpatient rehabilitation from acute hospitalisation; and (2) continue or cease inpatient rehabilitation. Methods: A web-based survey of CMOs practising in Queensland Australia, who were members of the Australian and New Zealand Society of Geriatric Medicine (n ~ 90) or the Queensland Stroke Clinical Network (n ~ 30) was completed. The survey contained two sections to explore factors that could: (1) favour or disfavour admission to inpatient rehabilitation from acute hospitalisation; and (2) favour continuation or cessation of inpatient rehabilitation. Open and closed questions were used. Results: Twenty-one CMOs (13–20% response rate, 43% geriatrician) completed the survey. Factors related to physical function, along with the presence of social supports favoured admission, while the presence of behavioural and cognitive impairments and a lack of staff capacity disfavoured admission. Improvements in function favoured continuation of inpatient rehabilitation, while a lack of improvement favoured cessation. Conclusion: Factors related to the patient, their social support network and the organisation were found to influence the decision of the CMO to admit a person with stroke to inpatient rehabilitation from acute hospitalisation. Once in rehabilitation, demonstration of benefit was consistently reported to indicate continued service need.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".