Chapter 1 A Comparison of Four Person-Environment Fit Models Applied to Older Adults
Bibliographic record
Abstract
Inthispaper, we comparefour Person-Environment(P-E) Fit models in terms of their ability to predict well-being among older adults. The models tested include: Lawton and Nahemow's (1973) competence model (model 1); Carp and Carp's (1984) congruence model (model 2); and two models based on Kahana's (1982) proposition that subjective prioritizing of multi-level environmental needs is a required element of P-E fit measures. The first priority model (model 3) represents unmet needs in the environmental domain with the highest subjective priority. The weighted priority model (model 4) scales P-E scores according to the prioritization of all environmentaldomains under study. A total sample of 174 seniors dwelling in the Vancouver community were used in this research; divided into a vulnerablesub-sample of Adult Day Care (ADC) clients (n = 115) and a non-frail community sub-sample (n = 59). Lawton's (1997) Valuation of Lifescale (VOL)was used as a measure of well-being. Model 4 was found to predict the largest amount of variance in VOL for the total sample and both sub-samples, after controlling for several covariates. Model 3 was the second best model in predicting VOL for the ADC sample, whereas model 2 wasthe second best predictor of VOL for the non-frail sample. The findings are discussed in terms of their implications for theory development, for explaining research showing that frail and non-frail older persons exhibit similar levels of well-being, and for client-centered service program-ming.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".