Evaluation of a congregate retirement residence and housing preferences of prospective occupants
Bibliographic record
Abstract
Due to the growing numbers of elderly people, the interest and concern with elderly housing has expanded.The situation of aged people in our modern society is unique.Most often, elderly people who are no longer healthy enough to live alone or no longer wish to maintain a residence must seek some sort of congregate housing.Different types of congregate housing are available -from retirement hotels to nursing homes.The two general purposes of this study were to determine: a) the factors which influence the propensity of an elderly person to move into retirement housing, and b) the features of a particular retirement facility that were important in predicting the satisfaction of pros- pective occupants with that facility.In addition, data concerning housing features preferences of the respondents were collected.This study dealt with a specific facility, called a retirement residence, which provided limited health, personal and social services.The follow- ing categories of variables, which were thought to be related to propensity to move were measured: a) demographic variables, b) respondent's satisfaction with the facility, and c) an index named the ideal resident index.This index was a deviation score computed for each respondent (potential user) which reflected the congruence between the respondent's actual demographic characterisitics and housing preferences, and the characteristics and preferences that the designers thought would be true of the users.The forty elderly people who participated in the study had all been visitors to the retirement residence under study.Questionnaires deal- ing with background information, housing preferences, reasons affecting the decision to move or stay and impressions of this particular retirement residence were mailed to respondents.Later, interviews employing visual displays were conducted with each respondent individually.The purpose of the interview was to a) gain information concerning the preferences of elderly with regard to physical appearance of facilities, b) get feedback on certain design features of this specific retirement residence, and c) to gather data about the respondents' expected usage of different spaces provided in the facility.A step-wise multiple regression analysis on propensity to move revealed that the ideal resident index was the most important predictor, while health and physical strength of the respondent was second most important.Together they accounted for 27.6 percent of the total vari- ance.Satisfaction with the facility was not significantly related to the propensity to move.A step-wise multiple regression analysis was also done on satis- faction with the facility.It was found that the most important factor in predicting satisfaction with the facility was satisfaction with the medical servcies provided at the facility.The second most important factor was a "convenience" factor composed of a) satisfaction with convenience of transportation facilities, b) satisfaction with con- venience of shopping facilities, and c) satisfaction with the amount of rent.Together, these two factors accounted for 47.3 percent of the total variance.The report has four major sections.For designers, managers and the retired resident readers, the last section, "Discussion" provides a brief overview of the research findings and suggestions for future re- search.The remaining sections are written for those interested in more theoretical and methodological explanations.These sections introduce the problem under study, the methodology employed, and the results of the data analysis.The appendix contains the questionnaire and the visual displays used for the interview with potential Friendship Hill residents.Friendship Hill provided a vehicle for the study of elderly housing issues in rural Illinois counties.Our team experienced the potentials as well as the difficulties of interdisciplinary team research.We believe that the fusion of many diverse backgrounds led to some innovative theoretical and methodological resolutions.We intend for this report to contribute to the cumulative effort in research and design profes- sions for creating better housing environments for older people.B1.INTERVIEWER'S SCRIPT " -" EVERYTHING you say will remain CON - FIDENTIAL ."-"Your name will not be attached to any of your answers.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".