Evaluation of patients´ perspective on a multimorbidity patient-centered care model piloted in the chilean public health system
Bibliographic record
Abstract
BACKGROUND: The progressive multimorbidity explosion has challenged Chile's health systems and worldwide. The Centro de Innovación en Salud ANCORA UC implemented a new Multimorbidity Patient-Centered Care Model in Chile. OBJECTIVE: Evaluate the perspective of high-risk patients about the core elements of the model. METHODOLOGY: We conducted a cross sectional telephone-based survey that considered the application of a 13 items questionnaire. Of them, nine were Likert scale questions with scores from 1 to 7, one dichotomic question, and three open-ended questions. 231 high-risk patients who received care through the model at primary care centers participated in the study. Quantitative data were encoded, consolidated, and analyzed with the SPSS software. We performed descriptive and analytic statistics techniques to assess different variables and their potential associations. Thematic analysis was conducted for qualitative data. RESULTS: The overall score was 5.84 (range: 1 to 7), with a standard deviation of 1.25. Questions with the best scores were those related with personalized care and the primary care teams. The lowest scored was for the item regarding the continuity of care between primary nurses and inpatient care at the hospital. There was a difference in patient outcomes depending on their health center. Regarding sociodemographic characteristics, age did not significantly affect the results. CONCLUSIONS: The study reveals the perceptions about a complex multimorbidity intervention from the patient's perspective. It complements the impact on health services utilization evaluation that supports decision-makers currently scaling up a similar strategy in our country and could be considered in other countries dealing with non-communicable diseases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".