How can students-as-partners work address challenges to student, faculty, and staff mental health and well-being?
Bibliographic record
Abstract
Mental health has emerged as a critical area of attention in higher education, and educational research over the last 15 years has focused increasingly on emotions and wellbeing at all stages of education (Hill et al., 2021). While definitions of well-being vary, most are premised on “good quality of life” (Nair et al., 2018, p. 69). Within the last few years, we have experienced an intersection of several forces that undermine or threaten good quality of life. These include the uncertainties prompted by the COVID-19 pandemic (Hews et al., 2022, U.S. Surgeon General, n.d.), climate change (Charlson et al., 2021), racism and social injustices (Williams & Etkins, 2021), the cost-of-living crisis (Montacute, 2023), and the lack of motivation and higher incidence of mental health issues associated with growing concerns about job prospects and income (Chowdhury et al., 2022). This fifth iteration of Voices from the Field explores some of the ways in which students-as-partners work can address challenges to the mental health and well-being of students, faculty, and staff. This focus, proposed by members of the IJSaP Editorial Board, both responds to the intersecting realities named above and remains true to the goal of this section of the journal, which is to offer a venue for a wide range of contributors to address important questions around and aspects of students-as-partners work without going through the intensive submission, peer-review, and revision processes. The prompt we included in the call for this iteration of Voices was: “In what ways can students-as-partners work address challenges to the mental health and well-being of students, staff, and faculty posed by the current realities in the wider world (socio-political, environmental, economic, etc.) that affect higher education?”
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Other design | low |
| grok | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| opus | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 3 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".