Bibliographic record
Abstract
Abstract The most recent national survey of older Cambodians was conducted in 2004. To further our understanding of Cambodia’s older adults, new data are required. This primary data collection study aimed to assess physical, psychological and social needs of older Cambodians and examine gender differences in those needs. We collected data in 2019 in three provinces in the northwestern region of Cambodia (N=210). Due to older Cambodians’ higher level of illiteracy, research assistants filled out paper surveys for them during individual interviews. Then responses were transferred into an e-survey database for data management. Physical needs were examined with self-rated health and health complaints. Psychological wellbeing and depression scores were used to measure psychological needs. For social needs, we examined social support using Social Network and Social Support scale. More than half of older Cambodians had less than good physical health and reported numerous health symptoms. Yet, three quarter reported higher than average psychological wellbeing and a low level of depression. Some of the samples showed modest level of social support in their family and community. Furthermore, independent two-sample T-tests showed that older men and women did not differ in physical and social needs (p<.001). Older women, however, did poorly in psychological needs compared to men (p<.001). Men also had lower level of depression and better psychological wellbeing (p<.001). Future studies shall further investigate other aspects of Cambodia’s aging population in which gender differentials may also exist.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".