Healthcare professionals providing care coordination to people living with multimorbidity: An interpretative phenomenological analysis
Bibliographic record
Abstract
AIMS AND OBJECTIVES: To explore the healthcare professionals (HCP) experience of providing care coordination to people living with multimorbidity. BACKGROUND: There is increasing interest in improving care of people living with multimorbidity who need care coordination to help manage their health. Little is known about the experiences of HCP working with people living with multimorbidity. DESIGN: Phenomenological approach to understanding the experiences of HCP. METHODS: We interviewed 18 HCP, including 11 registered nurses, working in care coordination in Melbourne, Australia. We used interpretative phenomenological analysis to identify themes from descriptions of providing care, identifying and responding to a person's needs, and the barriers and facilitators to providing person-centred care. RESULTS: We identified four themes as follows: (a) Challenge of focusing on the person; (b) "Hear their story," listening to and giving time to clients to tell their story; (c) Strategies for engagement in the programme; and, (d) "See the bigger picture," looking beyond the disease to the needs of a person. Our results are reported using COREQ. CONCLUSIONS: The HCP experienced challenges to a traditional approach to care when focusing on the person. They described providing care that was person-centred, and acknowledged that optimal, guideline-oriented care might not be achieved. They took the necessary time to hear the story and see the context of the person's life, to help the person manage their health. RELEVANCE TO CLINICAL PRACTICE: For registered nurses in care coordination programmes, focusing on the client may challenge traditional approaches to care. Providing care involves developing a relationship with the client to optimise health outcomes. Experienced registered nurses appear to use skills in reflective practice and accept the parameters of care to improve the client's health and well-being.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".