Better understanding care transitions of adults with complex health and social care needs: a study protocol
Bibliographic record
Abstract
BACKGROUND: Adults with chronic conditions who also suffer from mental health comorbidities and/or social vulnerability require services from many providers across different sectors. They may have complex health and social care needs and experience poorer health indicators and high mortality rates while generating considerable costs to the health and social services system. In response, the literature has stressed the need for a collaborative approach amongst providers to facilitate the care transition process. A better understanding of care transitions is the next step towards the improvement of integrated care models. The aim of the study is to better understand care transitions of adults with complex health and social care needs across community, primary care, and hospital settings, combining the experiences of patients and their families, providers, and health managers. METHODS/DESIGN: We will conduct a two-phase mixed methods multiple case study (quantitative and qualitative). We will work with six cases in three Canadian provinces, each case being the actual care transitions across community, primary care, and hospital settings. Adult patients with complex needs will be identified by having visited the emergency department at least three times over the previous 12 months. To ensure they have complex needs, they will be invited to complete INTERMED Self-Assessment and invited to enroll if positive. For the quantitative phase, data will be obtained through questionnaires and multi-level regression analyses will be conducted. For the qualitative phase, semi-structured interviews and focus groups will be conducted with patients, family members, care providers, and managers, and thematic analysis will be performed. Quantitative and qualitative results will be compared and then merged. DISCUSSION: This study is one of the first to examine care transitions of adults with complex needs by adopting a comprehensive vision of care transitions and bringing together the experiences of patients and family members, providers, and health managers. By using an integrated knowledge translation approach with key knowledge users, the study's findings have the potential to inform the optimization of integrated care, to positively impact the health of adults with complex needs, and reduce the economic burden to the health and social care systems.
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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 | 0.078 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.037 | 0.008 |
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".