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Record W7115320135

Mental Health Conditions of Women from the Anglophone Regions of Cameroon Related to the Secessionist Armed Conflict

2024· other· en· W7115320135 on OpenAlexaboutno aff

Bibliographic record

VenueDoria (University of Helsinki) · 2024
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthArmed conflictQuarter (Canadian coin)Occupational safety and healthMental illnessSuicide preventionInternally displaced person
DOInot available

Abstract

fetched live from OpenAlex

Aim: The aim of the study is to investigate how the mental health conditions of women in the Anglophone regions of Cameroon are related to the secessionist armed conflict that has been ravaging these regions since the last quarter of 2016. Method: Three hundred and two women from four cities in the restive Anglophone regions of Cameroon and Douala, one of the Francophone regions, completed a questionnaire measuring PTSD. Results: Among the respondents, 74.8 percent were internally displaced, 55.3 percent had been kidnapped, and 36.1 percent had been raped during the conflict. Women who had been kidnapped or raped scored significantly higher than others on symptoms of PTSD. Kidnapped women reported fear, horror, anger, or shame, and trouble remembering the conflict. Women who had been raped scored higher than others on irritability, aggressivity, lack of experiencing positive or loving feelings, strong physical reactions when reminded of the conflict, and trouble remembering the conflict. A correlation was also found between symptoms of PTSD and age. Conclusions: The study revealed that the mental health conditions of women from the Anglophone regions of Cameroon are related to the secessionist armed conflict currently ravaging said regions. Key Words: Anglophone crisis, Human Rights, Internally displaced women, Kidnap, Rape, Mental Health.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.284
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.014
GPT teacher head0.254
Teacher spread0.240 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

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