Exploring the Influence of Perceived Ingroup and Outgroup Threat on Quality of Life in a Region Impacted by Protracted Conflict
Bibliographic record
Abstract
While the detrimental effects of protracted political conflict on the wellbeing of Palestinians living in the occupied Palestinian territory (oPt) are generally recognized, the impact of perceived threat on quality of life (QoL) faced from within their community (ingroup; Palestinians) and from the outgroup (Israelis) is unexplored. This cross-sectional study examined the following: (1) The status of perceptions of QoL on four domains measured by the World Health Organization Quality of Life (WHOQoL-Bref) instrument, physical health, psychological health, social relationships, and environment, among Palestinian adults (n = 709) living in the Gaza Strip; (2) The associations between perceived ingroup threat (PIT) and QoL on the four domains; (3) The associations between perceived outgroup threat (POT) and QoL on the four domains. Multivariable linear regression models revealed PIT was negatively associated with QoL in each of the four domains (p < 0.001). POT was positively associated with QoL in three of the four domains: physical health (p < 0.001), psychological health (p < 0.001), and social relationships (p < 0.001). This study contributes valuable insights into how QoL is viewed by a group experiencing collective existential threat. The findings expand the limited recognition of the reciprocal roles of perceived threat from the ingroup and outgroup on the QoL of vulnerable populations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".