Bibliographic record
Abstract
Throughout this school year, junior level social work students have worked in partnership with various Mental Health Stakeholders and Agencies across Utah to develop and conduct a general public, statewide survey examining the knowledge, attitudes, and experiences related to mental health. Specifically, we examine how perceived barriers to mental health access and help-seeking might vary by gender. Research shows men and women respond to mental health concerns differently. Women are more likely to seek help from a mental health professional than men (Rhodes, Goering, To, & Williams, 2002). According to Slaunwhite (2015), women are more likely to cite a lack of childcare or transportation as keeping them from seeking help, while men tend to be kept away from seeking help by their perceptions of the usefulness of such services. This study augments the literature by examining these issues in a Utah context. Surveys were administered door-to-door in Logan, Brigham City, Price, Tooele, and Blanding, as well as online through social media and community and religious organization outreach throughout the state. The final sample size will consist of approximately 2,000 Utah residents aged 18 or older. The data from this study will be analyzed using SPSS statistical software. This presentation will examine and analyze how help-seeking behaviors and perceived barriers to accessing mental health services in Utah are affected by gender, and will propose ways in which this information can be used to inform mental health agencies throughout the state. Rhodes, A. E., Goering, P. N., To, T., & Williams, J. I. (2002). Gender and outpatient mental health service use. Social Science & Medicine, 54(1), 1-10. Slaunwhite, A. K. (2015). The role of gender and income in predicting barriers to mental health care in Canada. Community Mental Health Journal, 51(5), 621-627.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".