Challenges and opportunities of relationship centred care in health care settings. My journey and the evolution of my approach
Bibliographic record
Abstract
Understanding the complexities of relating in a health care context invites practitioners to anticipate and identify challenges and opportunities as they arise in their practice. This experiential paper will attempt to explore and illustrate some of the complexities of adopting a relationship centred approach in healthcare settings, from the perspective of one practitioner. This paper will consider how the organisational culture can impact on the practitioners ability to interact with patients. In this context the influence of the organisational culture with its emphasis on task, diagnosis and treatment of disease, functioned to undermine this practitioners capacity to relate and take up a relationship centred approach. This paper, with reference to clinical material, will highlight the tension that exists between task and relationship in healthcare settings. Special reference will be made to how in some situations the wish to relate interrupted the task focused work, causing co nfusion and great challenge for the practitioner . The invitation to adopt relationship centred practice , while still attending to the task in hand restored the practitioners belief in the medical consultation’s potential to create a receptive , responsive and relationship centred space . Finally, this paper will conclude by considering how to navigate this complex context and to achieve a balance which includes relationship centred care , using these opportunities as they arise to ensure optimum health care outcomes for both practitioner and patients.
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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 | 0.041 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.018 | 0.055 |
| Scholarly communication | 0.030 | 0.027 |
| Open science | 0.004 | 0.029 |
| Research integrity | 0.013 | 0.022 |
| 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".