Bibliographic record
Abstract
Aging is expected to be a priority health problem in Jordan because of accelerating number of this age group. Aging and dependency in performing daily activities are highly interrelated, especially in the presence of disabling health problems. Caring of elders group is a challenge health and social issue. Family members in Jordan provide elders with caregiving in their preferable living place. There is inadequate studies that describe who are the elders' caregiver in Arab or in Jordan community. The objective of this study was to describe the characteristics of elder's caregivers in Jordan context. A cross sectional descriptive study was conducted between October 2013 and February 2014. A convenience sample of Jordanian caregivers (n = 489) was recruited from health care center in Amman during follow-up health visits for their ill relatives. A self report questionnaire was used to collect data about socio-demographic characteristics of caregiver, caregiver's health, and additional data about care recipient. The majority of the sample were women (86.2%, n = 422). Women exhibited various undesirable life style practices, and they reported that they had diagnosed of different health problems. Women provided caregiving assistance for very long duration. It is recommended to extend the health follow up for caregiver in addition to elders' themselves. Training, education, and support caregiving program is encouraged for caregivers to continue their role with minimum suffering and to avoid negative physical, emotional, and social caregiving consequences.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".