Perceived risk factors of health decline: a qualitative study of hospitalized patients with multimorbidity
Bibliographic record
Abstract
Kerry Kuluski,1,2 C Shawn Tracy,1 Ross E Upshur1–3 1Bridgepoint Collaboratory for Research and Innovation, Bridgepoint Active Healthcare, Toronto, ON, Canada; 2Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada; 3Department of Family and Community Medicine, University of Toronto, Toronto, ON, Canada Background: Effectively preventing and managing chronic illness are key goals for health systems worldwide. A growing number of people are living longer with multiple chronic illnesses, accompanied by a high degree of treatment burden and heavy use of health care resources. People with multimorbidity typically have to manage their care needs for a number of years, and from this experience may offer valuable perspectives on factors that influenced their health outcome. Purpose: The purpose of this study was to explore factors that may serve as tipping points into poor health from the perspective of hospitalized patients with multimorbidity. Participants and methods: Patient interview data were analyzed from 43 hospitalized patients with multimorbidities who indicated that something could have been done to either avoid or slow down their health decline. The study used qualitative description as the analytic method to generate themes from a specific question collected through one-on-one interviews. Two reviewers independently analyzed and thematically coded the data and reached consensus on the final themes after a series of meetings. Results: According to patient accounts, factors at the personal level (eg, personal behaviors), provider level (eg, late diagnoses), and health care system level (eg, poor care transitions) contributed to their health decline. Conclusion: This paper focuses on prevention in the context of multimorbidity. While some respondents indicated personal behaviors that impacted health, many pointed to factors outside themselves (providers and the broader health system). The orientation of health care systems, historically designed to support acute and episodic care and not multimorbidity, places patients, at least in some cases, at additional risk of decline. The patient accounts suggest that the notion of prevention should evolve throughout the course of illness. A successful health system would embrace this notion and see the goal as forestalling not only mortality (as achieved for the most part in high socioeconomic nations) but morbidity as well. High rates of multimorbidity and health system challenges suggest that we have not yet achieved this latter aim. Keywords: Canada, chronic illness, multimorbidity, determinants of health, health service settings, patients
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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 | 0.021 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".