Psychosocial Influences on Patient Presentations: Considerations for Research and Evaluation at Mass-Gathering Events
Bibliographic record
Abstract
AIM: This review discusses the need for consistency in mass-gathering research and evaluation from a psychosocial perspective. BACKGROUND: Mass gatherings occur frequently throughout the world. Having an understanding of the complexities of mass gatherings is important to determine required health resources. Factors within the environmental, psychosocial, and biomedical domains influence the usage of health services at mass gatherings. A standardized approach to data collection is important to identify a consistent reporting standard for the psychosocial domain. METHOD: This research used an integrative literature review design. Manuscripts were collected using keyword searches from databases and journal content pages from 2003 through 2018. Data were analyzed and categorized using the existing minimum data set as a framework. RESULTS: In total, 31 manuscripts met the inclusion criteria. The main variables identified were use of alcohol or drugs, crowd behavior, crowd mood, rationale, and length of stay. CONCLUSION: Upon interrogating the literature, the authors have determined that the variables fall under the categories of alcohol or drugs; maladaptive and adaptive behaviors; crowd behavior, crowd culture, and crowd mood; reason for attending event (motivation); duration; and crowd demographics. In collecting psychosocial data from mass gatherings, an agreed-upon set of variables that can be used to collect de-identified psychosocial variables for the purpose of making comparisons across societies for mass-gathering events (MGEs) would be invaluable to researchers and event clinicians.
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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 | 0.058 | 0.150 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".