PLANNING HEALTH SERVICES FOR SENIORS: CAN WE USE PATIENTS’ OWN PERCEPTION?
Bibliographic record
Abstract
Across Canada, people over 75 years of age represent 16% of all hospital admissions. The admission rate per 100,000 seniors is 5 times higher for acute care and 22 times higher for complex continuing care than the rates for younger adults. After a hospital discharge, dealing with new disabilities can be difficult and even overwhelming. Understanding patients’ needs in a timely manner may allow services to be allocated to those at highest risk for deterioration, thus, improving care while optimizing health care cost. Could patient’s perceptions of how they are feeling be used as a marker of potential need for post-discharge services? The aim of the study was to estimate whether self-reported health can be used as an indicator of service needs among seniors. In this cross-sectional survey, age and sex-adjusted logistic regression was used to estimate the link between functional status indicators and fair or poor self-reported health. Results were reported as Odds Ratio (OR) and its 95% confidence interval (95%CI). Backward stepwise logistic regression was performed to identify the best predictive model of service needs. Positive predictive value (PPV), sensitivity and specificity were calculated to identify whether health perception could be used to identify people in need of physical rehabilitation services. 142 seniors agreed to answer the survey yielding a response rate of 73%. Among the respondents (mean age 79 ± 7; 60% women), 40% rated their health as fair or poor. Seniors perceiving their health as fair or poor had higher odds of reporting impairments, activity limitations, and participation restricitions (OR ranging from 2.37; 95%CI 1.03-5-45 to OR 12.22; 95%CI 2.68–55.78) in comparison to those perceiving their health as good or better. The most significant predictors of service needs were community ambulation, household tasks, fatigue, and pain with 92% sensitivity and a maximum adjusted R-squared of 0.65. Self-rated health used as single-item showed a positive predictive value (PPV) of 1, sensitivity of 52%, and specificity of 100%. In conclusion, our results indicate that all seniors reporting fair or poor health have indicators of need for further rehabilitation services. This question may be an alternate way of querying about need as many older persons are afraid to report disability because of fear of further institutionalization.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".