Families as hospital care givers: A pilot in Turkey
Bibliographic record
Abstract
Objective: It is a common tradition that families as caregivers have been in the hospital in order to support patients. This study describes the services performed by family caregivers in surgical and medical wards of hospital.Methods: This is a descriptive study. The study includes 442 family caregivers selected by the simple random sampling, who agreed to participate in, and who have been providing care for their relatives at least 48 hours in a university hospital. Data were collected through a questionnaire conducted during face-to-face interviews and were analyzed by descriptive statistics methods.Results: It has been found out that family caregivers met almost all needs of hospitalized patients with their own will. The results show that family caregivers met the care needs of their hospitalized relatives mainly upon their own and patients’ will (relatively 51%, 22.9%). It has also been observed that some of these requirements were met upon doctors’ and nurses’ demands.Conclusions: It is important to know the requirements met by family caregivers at the hospital in terms of defining boundaries of care which can be provided by family caregivers and evaluating its results. Our findings could influence future plans of nursing managers, policy makers and/or health authorities.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 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".