Health Advocacy Project: Evaluating the Benefits of Service Learning to Nursing Students and Low Income Individuals Involved in a Community-Based Mental Health Promotion Project
Bibliographic record
Abstract
Poverty, along with other factors such as unemployment, work and life stressors, interpersonal violence, and lack of access to high quality health and/or social services all play a role in determining who develops a mental illness and for whom those symptoms persist or worsen. Senior nursing student preparing to enter the field and working in a service learning capacity may be able to influence early recovery and symptom abatement among those most vulnerable to mental illness. A consortium of community stakeholders and researchers collaboratively designed a 10-week mental health promotion project called the Health Advocacy Project (HAP). The project combines case management and system navigation support delivered by trained and highly supervised nursing students to individuals experiencing major depressive disorder (MDD) and/or post-traumatic stress disorder (PTSD). In this article, we present the findings of a qualitative fidelity evaluation that examines the effectiveness of nursing students in delivering the health advocacy intervention at the level and with the intensity originally intended. The findings demonstrate how the services of senior nursing students may be optimized to benefit our healthcare system and populations most at risk for developing MDD and PTSD.
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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 | 0.019 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".