Examining the Concept of Healthcare Trajectories in Older Adults With Major Neurocognitive Disorder Using the ‘6W’ Multidimensional Model of Care Trajectories: A Mixed Methods Systematic Review Protocol
Bibliographic record
Abstract
Introduction: The use of healthcare services by older adults with major neurocognitive disorder (MNCD) varies significantly throughout the disease process. The evaluation of healthcare trajectories, defined as the pattern of care use over time, allows for a better understanding of how people move through the healthcare system and facilitates the identification of potentially modifiable risk factors for suboptimal care trajectories. Objectives: The objectives of the review are to: 1) critically appraise and synthesize evidence on how healthcare trajectories of older adults with MNCD are measured and defined, using the ‘6W’ multidimensional model of care trajectories, and 2) examine how socioeconomic factors are considered in studies reporting on healthcare trajectories. Inclusion criteria: This review will consider community-dwelling older adults diagnosed with MNCD. The quantitative component will include studies reporting on healthcare trajectories, including at least 2 different care services and at least 3 time-points. The qualitative component will include studies reporting on healthcare trajectories from the perspective of patients or their informal caregivers. Methods: This review will follow the Joanna Briggs Institute mixed methods review approach. We will search EMBASE, MEDLINE, CINAHL, PsycINFO, and the Web of Science Core Collection for English or French articles. Independent reviewers will identify articles for inclusion, extract data, and assess quality. A convergent integrated approach to synthesis and integration will be used. Discussion and conclusion: The results will help anticipate patients’ needs, improve patient care, service planning and coordination, and understand inequities in MNCD care.
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 | 0.133 | 0.146 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.016 | 0.018 |
| Bibliometrics | 0.020 | 0.017 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.027 | 0.004 |
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".