Needs assessment of individuals with stroke after discharge from hospital stratified by acute Functional Independence Measure score
Bibliographic record
Abstract
PURPOSE: To determine the needs, barriers and facilitators of function in individuals with stroke after discharge from hospital. To examine the results stratified by the patient's acute score (<41, 41-80, >80) on the functional independence measure (FIM). METHOD: This was a cohort study of 209 patients who had been admitted to hospital because of stroke. Patients were interviewed following hospital discharge using a semi-structured interview and asked to complete and return a quantitative closed-ended survey. RESULTS: For most domains, frequencies of needs varied across the FIM groups. Combining all FIM groups, the interview showed needs related to: physical impairments (35%), time for recovery (33%), education (28%), medical advice (25%), therapies and services (21%), social needs (19%) and emotional needs (18%). From the interview, the most frequent barriers were physical impairments (55%) and emotional concerns (40%). Common facilitators were family support (54%), therapies and medical care (40%) and personal attitudes (22%). Additional needs from the survey concerned: IADL, mobility, ADL, recreation, finances, communication and employment. Additional barriers from the survey were: attitudes, social participation, environments and limited services. CONCLUSIONS: There is a large and varied number of needs and barriers following discharge from hospital that have planning and advocacy implications for rehabilitation teams.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".