Effect of COVID-19 pandemic on the implementation of a multimorbidity person-centered care model: A qualitative study from health teams’ perspective
Bibliographic record
Abstract
The COVID-19 pandemic has abruptly changed care priority and delivery, delaying others like the multimorbidity approach. The Centro de Innovación en Salud ANCORA UC, the Health National Fund, and the Servicio de Salud Metropolitano Sur Oriente implemented a Multimorbidity Patient-Centered Care Model as a pilot study in the public health network from 2017 to 2020. Its objective was to reorganize the single diagnosis standard care into a new one based on multimorbidity integrated care. It included incorporating new roles, services, and activities according to each patient's risk stratification. This study aims to describe the perception of the health care teams regarding the impact of the COVID-19 pandemic on four main topics: how the COVID-19 pandemic affected the MCPM implementation, how participants adapted it, lessons learned, and recommendations for sustainability. We conducted a qualitative study with 35 semi-structured interviews between October and December 2020. Data analysis was codified, triangulated, and consolidated using MAXQDA 2020. Results showed that the pandemic paused the total of the implementation practically. Positive effects were the improvement of remote health care services, the activation of self-management, and the cohesion of the teamwork. In contrast, frequent abrupt changes and reorganization forced by pandemic evolution were negative effects. This study revealed the magnitude of the pandemic in the cancelation of health services and identified the urgent need to restart chronic services incorporating patient-centered care in our system.
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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 | 0.018 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".