Writing place: a comparison of nursing research and health geography
Bibliographic record
Abstract
The concept of 'place', and general references to 'geographies of ...' are making gradual incursions into nursing literature. Although the idea of place in nursing is not new, this recent spatial turn seems to be influenced by the increasing profile of the discipline of health geography, and the broadening of its scope to incorporate smaller and more intimate spatial scales. A wider emphasis within the social sciences on place from a social and cultural perspective, and a wider turn to 'place' across disciplines are probably equally important factors. This trend is raising some interesting questions for nurses, but at the same time contributes some confusion with regard to imputed meanings of 'place'. While it is clear that most nurse clinicians and researchers certainly understand that place of care matters to their practices and patients, many diverse uses of 'place' are found within nursing literature, and contemporary understandings of the term 'place' within nursing are not immediately clear. It is in this context that this article plans to advance the discussion of place. More specifically, the aims of this paper are threefold: to critique 'place' as it appears in nursing literature, to explore the use of 'place' within health geography, whence notions of place and 'geographies of' have originated and, finally, to compare and contrast the use of 'place' in both disciplines. This critique intends to address a deficit in the literature, in this era of growing spatialization in nursing research. The specific questions of interest here are: 'what is "place" in nursing?' and 'how do concepts of place in nursing compare to concepts of place in health geography?'
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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 | 0.027 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.017 |
| Science and technology studies | 0.009 | 0.065 |
| Scholarly communication | 0.024 | 0.024 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".