Distribution and determinants of the utilization of senior residential care homes in Saudi Arabia: a cross-sectional study
Bibliographic record
Abstract
Background With the accelerated increase in the population of seniors aged 60 years or older in Saudi Arabia, understanding the utilization of senior residential care homes is crucial for improving service delivery and policy planning to meet the care transformation objectives of Vision 2030. Objective To assess the distribution and determinants of senior residential care home utilization across Saudi Arabia’s 13 administrative regions, focusing on predictors of functional dependency among different socio-demographic groups. Methods This study analyzed data from 283 Saudi individuals aged ≥65 admitted to social residential care homes in 2021. Variables included age, sex, education level, marital status, region, and reasons for service use. Statistical analyses comprised descriptive statistics, chi-square tests, independent t-tests, and multivariable logistic regression. Results The Makkah region had the highest number of senior residential care home users (56.8%; p < 0.0001). Most participants were men (67.8%), while women constituted 32.2%. The mean age was 78.9 years (SD = 10.6), with women being significantly older than men (p = 0.014). Illiteracy was prevalent (73.5%), particularly among women (82.4% vs. 69.3% for men; p = 0.006). Most participants were divorced (68.2%), with higher rates among men (84.9% vs. 33% for women; p < 0.0001). The primary reasons for utilizing residential care home services were old age and functional dependency (88.5% of men and 83.4% of women). Multivariable logistic regression indicated that being in the age group 75–84 years (odds ratio [OR] = 1.62, 95% confidence interval [CI] = 1.02–1.81, p < 0.001), 85 years and above (OR = 2.63, 95% CI = 1.28–3.11, p < 0.001), and being single (OR = 2.43, 95% CI = 1.14–5.13, p = 0.019) were significant predictors of old age and functional dependency. Conclusion The study highlights regional and socio-demographic variations in senior residential care home service utilization in Saudi Arabia, emphasizing the need for targeted interventions and policies aligned with Saudi Arabia’s Vision 2030 to enhance service accessibility and effectiveness for the aging population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".