Planning Health Services for Seniors: Can We Use Patient’s Own Perception?
Bibliographic record
Abstract
ObjectivesThe objectives of this study were to identify needs and to estimate whether self-reported health can be used as an indicator of service needs among seniors.MethodsThis was a 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. Forward stepwise logistic regression was performed to identify the strongest contributors of poor health. 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.Results142 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 restrictions (OR ranging from 2.37 95%CI: 1.03-5-45 to 12.22 95%CI: 2.68-55.78) in comparison to those perceiving their health as good or better. The strongest contributors for poor/fair health were depression, difficulty performing household tasks, pain, and dizziness (c-statistic = 0.91 and a maximum adjusted r-squared of 0.60). Self-rated health used as singleitem showed a positive predictive value (PPV) of 1, sensitivity of 52%, and specificity of 100%.ConclusionOur results indicate that all seniors participating in this study and reporting fair or poor health have indicators of need for further rehabilitation services. Asking patients to rate their own health 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 | 0.010 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".