Locating a geography of nursing: space, place and the progress of geographical thought
Bibliographic record
Abstract
Although traditionally, nursing research has paid little attention to geographical approaches, recent years have witnessed some initial research interest in the dynamic between nursing, space and place. Such research potentially represents the foundations of what may be termed a 'geography of nursing'. Although, to date, some novel and valuable perspectives have been gained into the spatial features of nursing, no consideration has been given to the theoretical development of, and basis for, a geography of nursing. Furthermore, no consideration has been given to philosophical heritage; the treatment of space and place in human geography and the insights that this may provide for the new field of research. In this context, this paper provides an historical review of geographical research and traces the evolution of how space and place have been conceptualized and operationalized by it. The paper outlines the emergence of a health geography subdiscipline and its own changing and diverse perspectives. In the final section, the central themes of the current geography of nursing are considered and, reflecting back on the theoretical concerns of contemporary human geography, the paper outlines some philosophies and theories on which future geography of nursing could be based. From a disciplinary perspective, one potential role of the geography of nursing is argued to be the maintenance of the relationship between health geography and mainstream health service and medical concerns, but in a place-sensitive, patient-sensitive and qualitative form.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".