Home Away from Home: How Undergraduate and Graduate Students Experience Space and Place in a new Health Sciences Building
Bibliographic record
Abstract
Buildings contribute in crucial ways to how students experience learning spaces. Four schools within a faculty (nursing, nutrition, occupational and public health, and midwifery) moved into a new Health Sciences building Fall of 2019. This new building created a unique opportunity to explore the intersection between higher education and learning space design, informed by concepts of space and place, and students' profession specific and interprofessional learning experiences in a new Health Sciences building. A qualitative descriptive design was used. All undergraduate and graduate students within the four schools were invited to participate. Focus groups were undertaken to gain a rich understanding of students' experiences and views of their space and place of learning. Data collection involved focus group data from profession specific participant users and interprofessional participant users. Inductive thematic analysis of focus group transcripts generated an initial coding scheme, key themes, and data patterns. Codes were sorted into categories and then organized into meaningful clusters. A building planning development project document relating to the vision, intentions, design, and planning for the new building provided content from which to view the study findings. The study data contributed to the conversation about space and place and its influence on higher learning within specific intraprofessional and interprofessional student groups and provided insight into the process of actualizing a vision for a new learning space and the resultant experiences and perceptions of students within that space/place.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".