Health and Well-Being in the Context of Health-Promoting University Initiatives: Protocol for a Mixed Methods Needs Assessment Study at Qatar University
Bibliographic record
Abstract
BACKGROUND: Health-promoting universities are dedicated to fostering learning environments and organizational cultures that support the physical and mental well-being of students, faculty, and staff. As students constitute the largest group within the university community, any policy intervention targeting them is likely to have a significant impact on the university as a whole. OBJECTIVE: This study aims to assess the health status and needs of Qatar University (QU) students using a comprehensive and holistic definition of health, informed by the perspectives of students, faculty members, and key informants. The ultimate goal is to inform evidence-based policies and services designed to improve students' physical and mental well-being. METHODS: An explanatory sequential mixed-methods research design will be used to conduct a comprehensive assessment of students' health status and needs. This assessment will consist of a quantitative component (a web-based health survey) administered to a convenience sample of students, and a qualitative component, including focus groups with students and faculty members, as well as interviews with key informants. Priority health issues and their determinants, identified through the quantitative assessment, will inform and guide the qualitative assessment to provide a deeper understanding of the various contexts and factors shaping them. Descriptive analyses (eg, proportions or means with SDs), comparative analyses (eg, t tests or chi-square tests), and association analyses (eg, linear, logistic, or Poisson regression models) will be used to analyze the quantitative data. Thematic analysis will be used in the qualitative assessments. Additionally, an environmental scan will be conducted to assess relevant facilities, services, and programs at the QU campus and the QU Primary Healthcare Corporation Center, as well as to review university policies and regulations that may affect students' health and well-being. Together, the needs assessment and environmental scan will inform the design of multilevel interventions, including health education and promotion programs, health services orientation, and proposed policy changes. RESULTS: Between March and December 2022, 812 students completed the web-based health survey. Data have been extracted, cleaned, and harmonized. Analyses to assess the extent of selection bias and the calculation of weights to account for this in all subsequent analyses have been completed (by December 2023). Following the completion of all quantitative data analyses (expected by the end of 2024), focus groups, interviews, and the environmental scan will begin in January-December 2025. CONCLUSIONS: This project will help identify and prioritize the health needs of QU students and their determinants, and inform relevant services and policies targeting these needs. By using comprehensive and context-appropriate methods, this project will contribute to QU's strategic efforts to become a Health-Promoting University. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/58860.
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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 | 0.069 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.042 | 0.010 |
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".