Towards a more place‐sensitive nursing research: an invitation to medical and health geography
Bibliographic record
Abstract
During recent years, nursing research has adopted and integrated perspectives and theoretical frameworks from a range of social science disciplines. I argue however, that a lack of attention has been paid in past research to the subdiscipline of medical geography. Although this may, in part, be attributed to a divergence between research priorities and foci, traditional 'scientific' geographical approaches may still be relevant to a wide range of nursing research. Furthermore, a recasting, redirecting and broadening of medical geography in the 1990s, towards what is termed health geography, has enhanced the discipline and provided a more cultural and expansive recognition of health, and a more comprehensive understanding of the dynamic relationship between people, health and place. Given the increasing range of places where health-care is provided and received, and some recent linkages made between nursing and place by nurse-theorists, these newer perspectives and concepts may be particularly useful for interpreting nurses' and patients' relationships both within and with a variety of healthcare settings and living spaces. Indeed, although a more place-sensitive nursing research is potentially a trans-disciplinary academic endeavor, a range of geographical approaches would be central to such a project.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.056 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.005 | 0.037 |
| Scholarly communication | 0.014 | 0.046 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.021 | 0.020 |
| 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".