Clinical geography: nursing practice and the (re)making of institutional space
Bibliographic record
Abstract
AIM: To present a geographical study that highlights the wide ranging spatial features of nursing agency. In turn, illustrate the further potential for geographical research to describe, support, challenge and guide clinical practice - particularly with regard to those 'everyday' activities and actions undertaken on a frequent basis. BACKGROUND: To provide a focus, and to anchor the study in existing clinical knowledge and debates, the role of nursing in the (re)making of institutional experiences and life is specifically explored. METHODS: In-depth semi-structured interviews were conducted with 15 nurses working in Buckinghamshire and West London, UK. The following specialties were represented: acute care including emergency (n = 3), midwifery (n = 3), children's nursing (n = 2), elderly care (n = 1), rehabilitation (n = 2), mental health (n = 3) and palliative care (n = 1). RESULTS: Nurses claimed to actively manipulate, normalize and recreate clinical spaces as part of their everyday therapeutic practice. Specifically, the range of agency employed by them falls under the following six categories: adjusting social composition; introducing 'normal' activities; providing private spaces; seeking private spaces; explaining clinical spaces; spaces for personal escape and wellbeing. IMPLICATIONS FOR NURSING MANAGEMENT: It is recommended that nurse leaders - including researchers, managers and clinical educators - explore geography as a source of social scientific evidence that sheds light on the complex nature of everyday professional practice. In this regard, some important disciplinary and structural issues are noted.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".