Designing a Women’s Refuge: An Interdisciplinary Health, Architecture and Landscape Collaboration
Bibliographic record
Abstract
University programs are currently faced with a number of challenges: how to engage students as active learners, how to ensure graduates are ‘work ready’ with broad and relevant professional skills, and how to support students to see their potential as agents of social change and contributors to social good. This paper presents the findings from a study that explored the impact of an authentic, interdisciplinary project with health, architecture and landscape students. This project facilitated students’ entrée into the lived experience of women and children requiring refuge services as a result of homelessness and/or domestic violence. Students collaborated with stakeholders from the refuge sector, visiting sites, undertaking individual research, exchanging ideas and problem-solving, to develop a design guide for a women’s refuge. Focus groups were conducted at the conclusion of the activity to gauge students’ perceptions of the value of the activity. Results indicated that the ‘hands-on’ and collaborative nature of the learning experience in a real-world context was valued, primarily due to its direct relevance to professional practice. Architecture and landscape participants reported an increase in their understanding and knowledge of refuge clients, and many expressed a commitment to further learning and contribution to the sector. Nursing students felt that the authentic learning experience helped prepare them for the ‘real world’ of practice and that it aided development of their professional identities and capacity to effect real-world change. The learning activity had a positive impact on knowledge acquisition and students’ confidence to act as agents of social change.
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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 | 0.009 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".