Impact of microgeography on communication dynamics in a healthcare environment
Bibliographic record
Abstract
BACKGROUND: For growing healthcare organisations, anchored resources-assets that are not easily movable-may complicate expansion and distort workflow patterns. We examine work patterns at a radiation oncology department of a major Canadian hospital. As this department doubled its size, healthcare providers remained bound to treatment planning rooms and radiation machines at the original site. This study examines workplace communication and interactions before and after the expansion. METHODS: We conducted regression analyses using a unique dataset merging email communications, badge swipes, office locations and organisation charts for individuals that routinely use the treatment planning room (n=232). We use a difference-in-differences framework to compare individuals' behaviours before and after the expansion. Our dependent variables were how often individuals accessed the treatment planning room and email volumes between two individuals. FINDINGS: We find an overall decrease in the use of the treatment planning room, though the effect was larger for those that moved away from it. Further, we find an increase in email communication for dyads of individuals separated in the move, but only if they belonged to different departments. PRACTICAL IMPLICATIONS: Our research points to complex interdependencies among healthcare providers, shedding light on how hospital expansion may have unintended consequences. Healthcare leaders should acknowledge that interaction patterns will be affected when healthcare providers are separated from each other or from anchored resources. Shifting to remote interactions may be adequate in some instances; in others, it may negatively affect work outcomes as well as the engagement and satisfaction of providers and patients.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".