A Multinomial Regression Model of Risk for Falls (RFF) Factors Among Filipino Elderly in a Community Setting
Bibliographic record
Abstract
The etiology of falls is multifactorial. Internationally, nurses are challenged to address this problem through assessment and intervention. Anchored on Pender's Health Promotion Model (1996 Pender , N. J. ( 1996 ). Health promotion in nursing practice () , 3rd ed. . Stamford , CT : Appleton & Lange Stanford . [Google Scholar]) and the McGill Model of Nursing developed by Dr. Allen (Gottlieb & Rowat, 1987 Gottlieb , L. , & Rowat , K. ( 1987 ). The McGill Model of Nursing: A practice-derived model . Advances in Nursing Science , 9 ( 4 ), 51 – 61 .[PubMed], [Web of Science ®] , [Google Scholar]), a hypothesized model was grounded to explain the relationship of environmental safety, depression, autonomy, and support system to the risk for falls of the Filipino elderly in a community setting. This study was conducted to test a model that describes the relationship of environmental safety, depression, autonomy, and support system to the risk for falls of Filipino elderly found in the community setting. A six-part, multiaspect questionnaire was administered to 125 elderly respondents from a community in Bulacan, Philippines. Using descriptive analysis, the demographic profile of the respondents was characterized. Multinomial regression analysis was used to test the model. A model with adequate fit emerged (F-ratio = 6.071), which revealed that only environmental safety (standardized β = .28 and p value =.001) and depression (standardized β = .24 and p value =.006) significantly impacts the risk for falls; autonomy and support system did not display any statistical significance and were not considered direct determinants of the risk for falls. With the results of the study, the researchers look forward to the risk for falls being decreased and managed through early identification of the risk factors. Also, the model would contribute in the efforts of nurses as it serves as a guide on how environmental safety and depression among elderly Filipinos relate to the risk for falls.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".