Alexithymia, Depression and Post-Traumatic Stress Disorder (PTSD) as Predictors of Cynicism among Internally Displaced Persons (IDPS) in Benue State, Nigeria
Bibliographic record
Abstract
This study examined alexithymia, depression and PTSD as predictors of cynicism among IDPs in refugee camps in Makurdi, Benue State. The study made use of primary source of data. A cross-sectional survey design was adopted for this study. Measures used were; Toronto Alexithymia Scale (TAS-20), Becks Depression Inventory (BDI), Harvard Trauma Questionnaire and Cynicism Scale (CS). The result showed alexithymia (β =0.20; t = 3.95, p < 0.05) and PTSD (β = 0.47; t =9.57, p < 0.01) significantly predicted cynicism while, depression (β = -0.1; t = -.19, p < 0.01) did not predict cynicism. Implications of the study portend improvements in policy formation. Based on the findings of the study, it was recommended that psycho-education for the IDPs is essential to prepare them for life after prolonged displacement. Engaging more IDPs across different camps located within the six geo-political zones in Nigeria should be considered in future research. Keywords: alexithymia, depression, post-traumatic stress disorder, cynicism, internally displaced persons
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".