Navigating the health care system: insights from consumers with multi‐morbidity
Bibliographic record
Abstract
ravenscroft ef (2010) Journal of Nursing and Healthcare of Chronic Illness 2 , 215–224 Navigating the health care system: insights from consumers with multi‐morbidity Aim. This study explored the perspective of people with multi‐morbidity on navigating the health care system in Ontario, Canada. Background. Delivering health care to people with one or more chronic conditions presents a major challenge and opportunity for health care today. System redesign is informed by information from many sources including a vast body of knowledge about chronic illness. However, there is limited understanding of how patients with multi‐morbidity experience navigating the health care system. Design and method. An interpretive descriptive design was used to explore how patients with multi‐morbidity experience navigating the health care system. Data were collected through minimally‐structured interviews and a demographic questionnaire with 20 adult participants with chronic kidney disease, and co‐existing diagnoses of diabetes mellitus, and cardiovascular disease, review of the participants’ health records, and secondary contextual data collection. Findings. Two main themes emerged through iterative, constant comparative analysis: navigating rough terrain and discovering how to manage the health care system. The findings of this study highlight the disjuncture and misalignments in the health care delivery system and the cumulative health care‐related burden of multiple chronic conditions for consumers. Conclusion. The perspective of patients with multi‐morbidity on navigating the health care system provides valuable insights into how the health care system may be redesigned to maintain and improve quality of care. Relevance to clinical practice. It is increasingly important for nurses to recognise and understand the impact of how health care is delivered on the access to and continuity of care for patients with chronic conditions and the work required from such patients.
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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 | 0.000 |
| 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".