Space, Place, and the Evidence Base: Part II—Rereading Nursing Environment Through Geographical Research
Bibliographic record
Abstract
This, the second and final article in the short health geography series, articulates how, moving beyond the models and assumptions associated with the metaparadigm of Nursing Environment, as a focused subdisciplinary approach, health geography might provide unique insights into nursing. A case study of a fictional yet somewhat typical children's hospital is presented and demonstrates some wide-ranging geographical issues and research questions (and hence potential geographical data) pertaining to nursing and the allied health professions. Indeed, this broad-brush approach is purposeful to make as many connections as possible to readers with varied theoretical, methodological, empirical, and practice expertise. In addition to the case study, to indicate further how geographical inquiry might locate quite comfortably in nursing research, the article also makes some initial and tentative connections between geography and an established nursing framework for the uptake of research evidence for practice. Although it is acknowledged that geographical inquiry should certainly never have the first call on researching the relationships between nurses and their environments, it is argued that its conceptual focus on space and place provides dedicated and detailed attention and a sound basis for a reformed, "spatialized" route to a more comprehensive understanding. Moreover, it is argued that it also demonstrates great versatility in terms of the scales and the subject matter with which it might engage. Some important issues certainly remain with respect to what might be the correct form of engagement between geographical and nursing research, but arguably, as a reformed disciplinary approach, health geography has the potential to provide a wealth of focused evidence for nursing practice.
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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 | 0.070 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.004 | 0.037 |
| Scholarly communication | 0.021 | 0.033 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.006 | 0.009 |
| 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".