Bibliographic record
Abstract
Increasingly, university students seek help for eating issues, and along with the eating issues, they often present with multiple underlying problems that require intensive support and challenge university resources. In this paper, I share highlights of my ongoing practice-research program, “It’s Not about Food” (INAF), designed to identify and address knowledge and social support needs of university women with self-identified eating issues, and the specific support rendered by upper level nursing students who served as peer facilitators. These highlights are contextualized through a short film depicting the peer learning process. Mixed-method evaluation of the project conveyed the meaning, effectiveness, and value of the INAF group. For women living with eating issues, the group became a safe zone enabling contemplation of personal and health changes, including the need for seeking outside support and guidance for nutritional and mental health concerns. From the perspectives of the nursing students as peer facilitators, the most salient finding was their learning how to preserve rapport, a strategy which helped them get to know the participants as persons beyond the eating issues, and to feel as though they are developing greater competency, professional satisfaction, and leadership capacity. The facilitators pondered the practicality of this type of therapeutic practice within their traditionally timepressed, task-focused clinical placements. In the final analysis, INAF provided participants and peer facilitators with a transformed view of self and global concerns, such as the need for prevention interventions targeting younger persons and support for men and older persons living with eating issues.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".