Alignment of patient‐centredness definitions with real‐life patient and clinician experiences: A qualitative study
Bibliographic record
Abstract
INTRODUCTION: Patient-centred care (PCC) has come to the forefront for many institutions, funding agencies and clinicians, and is integrated into care. Does a disconnect in understanding still exist between patients, healthcare organizations and clinicians in what PCC means and how outstanding issues might be addressed? METHODS: We conducted interviews and focus groups with self-reported chronic care patients and clinicians providing care to these patients exploring PCC experiences, expectations and practices. These data were initially analysed using inductive thematic analysis. This paper reports on the findings of a secondary analysis examining the alignment between patients and clinicians on five key predetermined dimensions of PCC. RESULTS: Eighteen patients participated, representing a range of chronic conditions. Thirty-eight clinicians participated. One thousand and three hundred patient and 1800 clinician codes were identified and grouped into 5 main topics with 140 unique themes (patients) and 9 main topics with 54 unique themes (clinicians). A total of 166 quotes (patient = 93, clinician = 73) were identified for this PCC definition alignment analysis. Partial or complete alignment of patient and clinician perspectives was seen on most dimensions. Key disconnects were observed in patient involvement, patient empowerment and clinician-patient communication. Only 18% of patients reported experiencing patient-centred communication, whereas 57% of clinicians reported using patient-focused communication approaches. CONCLUSION: Overall, study patients and clinicians endorse that many PCC elements occur. This study highlights key differences between patients and clinicians, suggesting persistent challenges. Clinician participants relayed their PCC approaches of informing and educating patients; however, patients often perceive these approaches as didactic, role-diminishing and noncollaborative. Collaborative approaches, such as shared decision-making, hold promise to bridge persistent PCC gaps and should be integrated into medical education programmes. PATIENT OR PUBLIC CONTRIBUTION: This project was conceived and executed with a co-design approach wherein patients with chronic conditions who are trained in research (i.e., see descriptions of Patient and Community Engagement Research in the text) were involved in all stages of the research project alongside other researchers on the project team. Healthcare providers were involved as participants and as principal investigators in the project.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".