Determinants of Subjective Health, Happiness, and Life Satisfaction among Young Adults (18‐24 Years) in Guyana
Bibliographic record
Abstract
. Persistent urban-rural disparity in subjective health and quality of life is a growing concern for healthcare systems across the world. In general, urban population performs better on most health indicators compared with their rural counterparts. However, research evidence on the urban-rural disparity on perceived health, happiness, and quality of life among the young adult population is scarce in South American countries like Guyana. Therefore, in the present study we aimed to investigate whether subjective health, happiness, and quality of life differ according to place of residence among the young adult population in Guyana. METHODS: Cross-sectional data on 2,434 men and women aging between 15 and 24 years were collected from the most recent Guyana Multiple Indicator Cluster Survey conducted in 2014. Outcome variables were perceived: satisfaction about health, life, and happiness, as well as life satisfaction before and after one year from the time of the survey. The urban-rural disparity in reporting satisfaction for these indicators was assessed by multivariate regression methods and by adjusting for relevant sociodemographic factors. RESULTS: More than four-fifth of the respondents reported satisfaction with health (82.4%) and life (81.4%) and 77.9% reported being happy. A vast majority expressed improvement in life situation compared with a year ago (81.4%), and nearly all of the participants (95.4%) expect to have better life situation a year later. Multivariate analysis revealed an inverse association between rural residence and subjective health among men [OR = 0.518, 95%CI = 0.297, 0.901], and happiness [OR = 0.662, 95%CI = 0.381, 0.845] and life satisfaction [OR = 3.722, 95%CI = 1.502, 9.227] among women. Women having secondary [OR = 2.219, 95%CI = 1.209, 3.720] and higher [OR = 1.600, 95%CI = 1.041, 3.302] education also had higher odds of satisfaction with happiness. CONCLUSIONS: Our findings demonstrate the existence of significant urban-rural disparities in perceived health and quality of life among the young adult population in Guyana, especially among women. National health promotion projects should therefore take proper policy actions to address the underlying factors contributing to the urban-rural gaps in order to establish a more equitable healthcare system. Further researches are necessary to explore the underlying causes behind such disparities.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".