Jordanian Nursing Homes: Admission Policy Analysis
Bibliographic record
Abstract
DESCRIPTION OF PROBLEM SITUATION: Although the elderly have many physical, mental, and psychosocial needs, nursing homes are still a new emergent concept in Jordan. Moreover, the elderly who have access to nursing homes, are often not admitted, based on specific criteria, nor have access to governmental funds. The elderly that are admitted to nursing homes are admitted for a myriad of reasons, such as families’ desires, referrals from the ministry of social welfare due to socioeconomic issues, health issues (i.e. disability), and absence of caregivers (Al-Qudah, 2011). What is lacking in Jordan, is a well-defined admission and screening tool that clearly defines eligibility for nursing home admission POLICY ALTERNATIVE: The policy alternative is leaving Jordanian elderly with special needs in their homes without receiving appropriate care predisposing the elderly to a higher risk of health complications. North Carolina has been chosen as an example of a state that implements a screening tool for admission. RECOMMENDATIONS: The Ministry of Social Development might tailor the NC Medicaid forms (Level I and Katz and MoCA, and Level II of the NC Medicaid screening tool) as an admission screening policy that could be successful in identifying the eligible older adults to admit to nursing homes and receive designated aids from the Jordanian governmental organizations.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".