Exploring Australian Hajj Tour Operators’ Knowledge and Practices Regarding Pilgrims’ Health Risks: A Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Travel agents are known to be one of the main sources of health information for pilgrims, and their advice is associated with positive health behaviors. OBJECTIVE: This study aimed to investigate travel agents' health knowledge, what health advice they provide to the pilgrims, and their sources of health information. METHODS: In-depth interviews were conducted among specialist Hajj travel agents in Sydney, Australia. Thematic analysis was undertaken. RESULTS: Of the 13 accredited Hajj travel agents, 9 (69%) were interviewed. A high level of awareness regarding gastrointestinal infections, standard hygiene methods, and the risk of injury was noted among the participants and was included in advice provided to pilgrims. However, very limited knowledge and provision of advice about the risk of respiratory infections was identified. Knowledge of the compulsory meningococcal vaccine was high, and all participated travel agents reported influenza vaccine (a recommended vaccine) as a second "compulsory" vaccine for Hajj visas. Conversely, participants reported very limited knowledge about other recommended vaccines for Hajj. The Ministry of Hajj website and personal Hajj experience were the main sources of information. CONCLUSIONS: This study identifies a potential path for novel health promotion strategies to improve health knowledge among Hajj travel agents and subsequently among Hajj pilgrims.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".