(Re)thinking the dynamics between healthcare and place: therapeutic geographies in treatment and care practices
Bibliographic record
Abstract
Although the concept of therapeutic landscapes has assisted health geographers to explore the intimate connections between well‐being and place, arguably, after a decade of applications, the common interpretations and assumptions of commentators should be reviewed. Based on theoretical insights from psychoanalytic geographies and geographies of nursing, this paper makes two observations. First, that landscape has been almost exclusively interpreted in a physical sense with co‐presence being a necessary condition. In turn, this assumption has led to the neglect of non‐physical (imagined) places. Second, that therapeutic effects have been interpreted as experiences attained outside of clinical practices. Hence, healthcare workers and their direct treatments and care have also been neglected. To explore these omissions together, an interview survey of complementary therapists investigates the many ways in which imagined places are constructed and manipulated in therapy sessions. Arguably, beyond this example, extending the therapeutic landscape concept to both physical and non‐physical features of treatments could provide fresh insights into the dynamics between healthcare and place. In disciplinary terms, the benefits are twofold. Whilst it could help develop a critical tradition in health geography, perhaps ironically, it could also provide a stronger disciplinary connection between qualitative health geography, various forms of medicine and their research traditions.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.068 |
| Scholarly communication | 0.014 | 0.021 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".