After discharge from hospital, where do we go? Follow-up of clients with mental illness and/or addiction: 30 days post-acute care.
Bibliographic record
Abstract
This retrospective descriptive research explores post-acute hospital follow-up for individuals with mental illness and/or addiction diagnosed in the Thompson Cariboo Shuswap Health Service Area in 2004/2005. The study examines follow-up rates determined by Performance Measure 5.1 and their differences as a function of other factors including diagnostic categories, Local Health Area hospitals, types of follow-up and number of separations. Data gathered from information systems in acute care hospitals, community mental health and addictions centres, and physicians' service billings of Medical Services Plan demonstrated an average of 75.9% follow-up. Variations occur between types of community follow-up and diagnostic categories. Findings indicate individuals diagnosed with substance use disorders have less follow-up whereas individuals with psychosis, bipolar disorder, anxiety disorders, depression, and disorders with early onset have more follow-up. These implications direct suggestions for post-acute discharge follow-up procedures and areas for further research.--P.ii.
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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 | 0.000 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".