Moving from space to place: Reimagining the challenges of physical space in primary health care teams in Ontario.
Bibliographic record
Abstract
OBJECTIVE: To extend our understanding of how primary health care team members characterize the effects of location on team functioning. DESIGN: Qualitative study using grounded theory methodology, with in-depth analysis of data concerning the role of physical space in teamwork. SETTING: Family health teams in Ontario. PARTICIPANTS: A total of 110 team members from 20 family health teams in Ontario. METHODS: Individual semistructured interviews were conducted. Interviews were audiorecorded and transcribed verbatim. Individual and group coding followed grounded theory processes of open, axial, and selective coding. Immersion in interview and field note data facilitated crystallization. MAIN FINDINGS: Across sites, regardless of their physical space, team members commented spontaneously about the role of space in team functioning. An overarching theme of a "sense of place" developed from data analysis. A sense of place could be established through co-location (being in the same physical space), the allocation of team members' working spaces, coming together, and having a shared vision. Physical space often operated as a key facilitator or considerable barrier to creating a sense of place; however, some teams with suboptimal physical space functioned as highly integrated teams, creating a sense of place through various means. CONCLUSION: Many interprofessional health care teams cannot physically change less-than-optimal spaces. However, teams can thrive and create a sense of place through various means, some of which relate to actual physical space, and some of which relate to promoting common activities and a shared vision-factors that are effective for team building in general. When there are economic limitations, as well as structural constraints, then it is essential that creating a sense of place be a priority. Future research should consider this lens as a means for expanding the discussion and possible solutions around traditional space 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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".