Through the lens of work-integrated learning: Staff experiences of participating in person-centredness coach training in a Swedish hospital
Bibliographic record
Abstract
Background/Objective: Over the past decade, many scientific articles have focused on the importance of person-centred care (or person centredness) in the health care sector. In practice, however, person centredness is difficult to operationalise. Thus, the role of “person-centredness coach” was created in a Swedish hospital to provide information, education, and reflection on person centredness. The aim was to describe this new role of a person-centeredness coach, and how the coaches experienced the development of a person-centred working method.Methods: Qualitative semi-structured individual interviews were conducted with nine nursing staff. The data were analysed using inductive content analysis.Results: The analysis resulted in three categories with seven subcategories: an eye opener (with the subcategories of a welcome change and person centredness throughout the organisation); an obstacle with potential (with the subcategories of theoretical vs. practical development of person centredness, difficulties in developing person centredness and proposals for promoting patient participation); and a challenging role (with the subcategories of necessary but a role that takes a long time to develop and the importance of favourable conditions).Conclusions: The person-centredness coaches believed that the person-centred approach was important and that it should be the foundation of all care work within health care but, despite this, had difficulty in integrating person centredness into their practice. The person-centredness coaches found the coach training rewarding. They perceived that, from a learning perspective and through the lens of work-integrated learning, the results could be related to creating praxis, which may be seen as a development area for further research in operationalising person centredness.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.024 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.003 | 0.006 |
| 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".