Self-Reported Alcohol Abuse and the Desire to Receive Mental Health Counselling Predict Suicidal Thoughts/Thoughts of Self-Harm among Female Residents of Fort McMurray
Bibliographic record
Abstract
Suicidal ideation and thoughts of self-harm continue to be challenging public health problems. It is presently unknown what the prevalence and correlates of suicidal thoughts and self-harm are in female residents of Fort McMurray, a city that has endured wildfires, flooding, and the COVID-19 pandemic in the last five years. This study aimed to determine the prevalence and correlates of suicidal ideation and thoughts of self-harm among female residents of Fort McMurray. A cross-sectional study using an online survey questionnaire was used to collect sociodemographic and clinical information from the residents of Fort McMurray between 24 April and 2 June 2021. Suicidal ideation and thoughts of self-harm among females were assessed using the ninth question of the Patient Health Questionnaire-9, a validated screening tool used to assess depression symptoms. Likely generalized anxiety disorder (GAD), major depressive disorder (MDD), post-traumatic stress disorder (PTSD) and low resilience were measured using standardized rating scales. Data were analyzed with SPSS version 25 using chi-squared tests and multivariate logistic regression analysis. Among Fort McMurray residents, 249 accessed the online survey, while 186 ultimately completed it, yielding a survey completion rate of 74.7%. Of these, 159 (85%) were females. After controlling for other variables in the regression model, respondents who expressed a desire to receive mental health counselling were more than seven times more likely to report suicidal ideation and thoughts of self-harm compared to the respondents who did not desire to receive mental health counselling (OR: 7.29; 95% CI: 1.19-44.58). Similarly, respondents who reported having abused alcohol in the past year were nearly four times more likely to report suicidal ideation or thoughts of self-harm compared to the respondents who said they had not abused alcohol in the past year (OR: 3.91; 95% CI: 1.05-14.57). A high prevalence of suicidal thoughts and thoughts of self-harm were reported among female residents of Fort McMurray. Timely access to adequate mental health support should be offered to female residents of communities impacted by multiple natural disasters, particularly residents who self-report alcohol abuse or desire to receive mental health counselling.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".