Survey of current pre-discharge home visiting practices of occupational therapists
Bibliographic record
Abstract
BACKGROUND/AIM: Discharge planning frequently involves occupational therapy pre-discharge home visiting as one component of intervention. Pre-discharge home visits aim to maximise a person's functional performance within the context of their home and community environment, bridging the transition between hospital and home. The aim of this study was to describe the pre-discharge home visiting practices of occupational therapy departments. METHODS: This descriptive study used a postal survey which was sent to occupational therapists in 215 public and privately funded hospitals in New South Wales, Australia. The survey enquired about the number of pre-discharge home visits completed per month, who went on visits and time spent on visits. Descriptive statistics were used in analyses. RESULTS: Surveys were returned by occupational therapists from 53 departments, representing a response rate of 25%. Respondents estimated that they conducted approximately 13 pre-discharge home visits per month (range: 1-60). Visits were estimated to take an average of 1 hour and 20 minutes (excluding travel time). Approximately one-quarter of respondents felt that there was pressure to reduce the number of pre-discharge home visits conducted. Using their local hospital records, nine hospital departments estimated that the number of home visits completed per month had reduced by 50% compared with the number of home visits five years previously. DISCUSSION: Findings suggest a wide variation in current pre-discharge home visiting practice. There is a need for well-designed clinical trials that investigate the effectiveness of these costly and time-consuming visits on functional performance.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 0.001 |
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".