Home Health Care (HHC) Managers Perceptions About Challenges and Obstacles that Hinder HHC Services in Jordan
Bibliographic record
Abstract
UNLABELLED: Home care aims at supporting people with various degrees of dependency to remain at home rather than use residential, long-term, or institutional-based nursing care. Demographic, epidemiological, social, and cultural trends in Jordan as in other countries are changing the traditional patterns of care with growing emphasis on home care. The purpose of this study is to highlight the most common challenges related to home health care (HHC) services in Jordan as perceived by the managers of HHC agencies. METHODS: A descriptive qualitative design that depends on focus group discussions has been used to collect data from a sample of 18 managers who met the selection criteria and who are willing to participate, the study found that, the main challenges of HHC services as perceived by managers were: shortage of female staff, lack of governance and regulation, poor management, unethical practices, lack of referral systems, and low accessibility of the poor and less privileged as HHC services are not included in health insurance schemes, it concludes also that the home health care industry in Jordan is facing many challenges and problems that may have negative effects on the effectiveness, efficiency, equity and quality of services and should be addressed by health policy makers.
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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 | 0.005 |
| 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 | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".