ACHIEVING PERSON-CENTRED LONG-TERM CARE THROUGH VOLUNTEERING
Bibliographic record
Abstract
Person-centred care (PCC) is designed to focus care delivery on the needs and preferences of the individual. Transferring PCC principles into practice is especially difficult when staffing levels are insufficient to allow time to respond to individual needs. The objective of this project was to develop and implement a volunteer program that sensitizes students to person-centred approaches including an emphasis on language. University students were recruited to volunteer for three hours, twice a week, for the duration of 12–18 months. Each resident was paired with two volunteers who focussed on improving access to residents’ preferred activities, interaction with other residents and participation in home life. Continued learning for volunteers was maintained through monthly discussion groups with experts and fellow volunteers. Data was collected through journal entries, focus groups every three months, MDS data on residents and Likert scales assessing volunteer interest in working in this field. Results indicate a strong preference for one-on-one volunteer work as well as extended volunteering experience. Volunteers spoke of acting as advocates and facilitators for residents; and families spoke of respite for their visits. Continued learning was expressed as a benefit to students. Residents were able to participate in activities not normally available to them, such as outdoor activities. The results and lessons learned, within the context of PCC, as well as the avenues for future research and standardization of approach will be discussed.
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 | 0.017 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".