Translating theory into clinical practice: a qualitative study of clinician perspectives implementing whole person care
Bibliographic record
Abstract
Whole Person Care (WPC) is an emerging framework that emphasises the clinician's role in empowering patient healing. However, reliably translating a framework's theory into practice is a recognised challenge for clinicians. Observational studies have revealed discrepancies between a clinician's stated values in theory and how these may be implemented in practice. The aim of this qualitative study is to bridge the gap between the theory of WPC and its practical implementation by clinicians. We interviewed a diverse group of 34 clinicians attending the 2017 International Whole Person Care Congress to explore (1) their conceptions of WPC in theory as well as (2) how they monitor their practice in real time. Data were analysed using Grounded Theory Methodology. Preliminary results were presented in the form of a workshop at the 2019 International Whole Person Care Congress to validate our findings with relevant stakeholders. The results revealed a vision of WPC that highlighted themes of the clinician's way of being, seeing the person beyond the disease, and the clinician-patient relationship. Our results demonstrate that clinicians use a range of strategies to monitor their practice in real time. Mindfulness and self-awareness were frequently cited as being crucial to this ability of self-regulating their practice. This study helps establish a unifying framework of WPC based on a diverse range of clinician-reported experiences. More importantly, it sheds light on the range of strategies employed by clinicians who monitor their practice in real time. These collected insights will be of interest to any clinician interested in translating their stated values into their clinical practice more reliably.
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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 | 0.059 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.016 | 0.020 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.005 | 0.010 |
| 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".