Implementation and impacts of virtual team-based care planning for older persons in formal care settings: A scoping review
Bibliographic record
Abstract
Objective: This scoping review aimed to summarize current knowledge about the implementation, impacts, facilitators and barriers of virtual team-based care planning for older persons in formal care settings (e.g. home and community, primary, long-term and acute care). Methods: The Joanna Briggs Institute (JBI) methodology was used. The Arksey and O'Malley and Levac, Colquhoun, and O'Brien methodologies provided additional frameworks. Databases accessed included PubMed, EMBASE, CINAHL, AgeLine, PsycInfo and Scopus. Reference lists of selected articles and grey literature retrieved through Google and Google Scholar were also reviewed. Three researchers screened titles, abstracts and conducted full-text reviews. Extracted data were mapped in a table and analysed for summative themes. Older persons and family partners assisted in interpreting findings based on their lived experiences. Results: A total of 27 studies were included. Virtual team-based care planning led to many positive outcomes for older persons (e.g. decreased depression, reduced falls and improved medication management) and their families (e.g. reduced caregiver stress and improved caregiving skills). Only four studies reported the involvement of older persons and/or families in virtual team-based care planning. Multiple barriers to adopting virtual team-based care planning were found including lack of education/training for older persons and families in using technology. Conclusion: Despite the multiple advantages that virtual team-based care planning offers for older persons and families, it is important to ensure that this care can be offered to all. There is a need to ensure that health equity is addressed to promote access to care and respond to social determinants of health.
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 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".