What are healthcare providers' understandings and experiences of compassion? The healthcare compassion model: a grounded theory study of healthcare providers in Canada
Bibliographic record
Abstract
Background Healthcare providers are considered the primary conduit of compassion in healthcare. Although most healthcare providers desire to provide compassion, and patients and families expect to receive it, an evidence-based understanding of the construct and its associated dimensions from the perspective of healthcare providers is needed. Objectives The aim of this study was to investigate healthcare providers’ perspectives and experiences of compassion in order to generate an empirically derived, clinically informed model. Design Data were collected via focus groups with frontline healthcare providers and interviews with peer-nominated exemplary compassionate healthcare providers. Data were independently and collectively analysed by the research team in accordance with Straussian grounded theory. Setting and participants 57 healthcare providers were recruited from urban and rural palliative care services spanning hospice, home care, hospital-based consult teams, and a dedicated inpatient unit within Alberta, Canada. Results Five categories and 13 associated themes were identified, illustrated in the Healthcare Provider Compassion Model depicting the dimensions of compassion and their relationship to one another. Compassion was conceptualised as—a virtuous and intentional response to know a person, to discern their needs and ameliorate their suffering through relational understanding and action. Conclusions An empirical foundation of healthcare providers’ perspectives on providing compassionate care was generated. While the dimensions of the Healthcare Provider Compassion Model were congruent with the previously developed Patient Model, further insight into compassion is now evident. The Healthcare Provider Compassion Model provides a model to guide clinical practice and research focused on developing interventions, measures and resources to improve it.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".