A Qualitative Analysis of Factors Influencing the Intention of Selecting the Charged Nursing Care Facilities
Bibliographic record
Abstract
Objective: To provide information of charged nursing care facility for helping to establish geriatric health care policy, and to figure out which factors would be the main determinants for the choice of it. Method: 46 males and 53 females, and the same number of their caregivers admitted into the charged nursing care facility were recruited for intensive interview including personal information, disease information, and economic, familial, marital and emotional statuses. This is a cross sectional study and we analyzed the data qualitatively. Results: Patients had 3.2 diseases and a hospitalization for 2.3 years on average. They were consists of 46 singles (46.9%), 8 unmarried (8.2%), 5 divorced (5.1%) and 32 married (32.7%). More than two third (70.1%) were supported by their eldest son or daughter. Mostly, the family caregivers decided to admit into the facilities by the doctor's recommendation (68.4%). When they made a choice for a facility, most of them (42.9%) considered environmental and sanitary conditions. According to their expectation for management in nursing care facility, most caregivers (59.2%) wanted simple-staying for the duration, but most patients (61.3%) expected to be home after taking comprehensive rehabilitation. Three quarter of the caregivers would agree to use nursing care facilities in the future, if they would be the same situation. Conclusion: Life style and environment are rapidly changing. In the near future, we need lots of the charged nursing care facilities for the old, thus this study can be the good reference for the preparing upcoming aged and super aged society.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".