Adaptation of Practice Guidelines to Prevent Functional Decline in Hospitalized Elderly in Iran
Bibliographic record
Abstract
Background: In Iran, many efforts have been made to improve the Quality of Life (QOL) of the elderly; however, despite the efforts made, there is no practice guideline based on the consensus of experts that can be used to prevent the functional decline of hospitalized elderly. Accordingly, the present study was conducted with the aim of adaptation of a practice guideline to prevent the functional decline of hospitalized elderly. Materials and Methods: This study is a developmental study based on the adaptation steps of the practice guideline. First, a search was conducted in 8 databases. The only practice guideline that met the inclusion criteria was then evaluated by the research team using the Appraisal of Guidelines for REsearch and Evaluation (AGREE II) tool. After content analysis of this guideline, the recommendations were categorized in the Canadian Senior Friendly Care (sfCare) Framework and according to the community conditions. Relevant evidence was used to supplement the content. The draft practice guideline was evaluated and modified in two expert panels through the RAND technique. Results: The categorized recommendations were developed in the eight chapters of introduction to the prevention of functional decline of the elderly, general practice guideline, organizational support, care processes, physical ecology, emotional and behavioral environment, ethics in care, and evaluation of function. Conclusions: To prevent functional decline in hospitalized elderly individuals according to the adaptive practice guideline, the hospital and health team need to be aware of support, care processes, and effective function appraisal to be able to provide care with coherent and coordinated solutions.
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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 | 0.043 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".