A population‐based comparison of the use of acute healthcare services by older adults with and without mental illness diagnoses
Bibliographic record
Abstract
ACCESSIBLE SUMMARY: Older adults with mental illness (MI) are a highly vulnerable population and need to be provided healthcare services in a timely and thorough way. Compared with older adults without MI, older adults with MI spend a great deal of time being hospitalized and hence costing millions of dollars because the care they need is often overlooked and/or not provided. While they end up spending too much time in hospital, in the emergency department and getting readmitted to hospital because of their MI, this could have been prevented or lessened if an adequate assessment and treatment regime was done by clinicians who were well informed on the topic of older adults' mental health. Older adults with MI are also more likely to leave hospital for long-term care settings, to die and to have more sickness compared with older adults who do not have MI. Further, they are also more likely to be admitted to hospital on an urgent, unplanned basis. How older adults with MI use acute hospitals is important for psychiatric nurses to know and understand, as they can help to provide the care needed so they do not have to be in hospital for long periods of time. Psychiatric nurses can share much support and information on making sure older adults with MI are accurately care for when needed. To explore and compare predictors of hospital length of stay (LOS), acute LOS (ALOS), emergency room (ER) wait times, rate of readmission (ROR) and costs of inpatient hospital care for older adults with and without mental illness (MI) diagnoses in the province of Newfoundland and Labrador (NL). This descriptive-comparative study used aggregate population level data of 12,283 people aged 65 years and older admitted to an acute care hospital in the province of NL. A total of 8.3% of hospitalized older adults had MI diagnoses. Older adults with MI diagnoses had a significantly longer LOS, ALOS, ROR, ER wait time and costs compared with older adults without MI diagnoses, after controlling for medical co-morbidities. Key variables such as patient demographics, admission indicators, discharge indicators and other medical co-morbidities had differential impacts on observed service use. While only a small percentage of hospitalized older adults had MI diagnoses, the use and cost of acute hospitalizations was significantly greater than that of older adults without MI diagnoses.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".