Bibliographic record
Abstract
Abstract Heightened isolation during the pandemic has exacerbated the stress, anxiety, and adverse consequences through the loss of family connections older people experience in LTC. Heavy workload and staffing shortage limit staff’s capacity to assist residents in accessing regular virtual visits. Using a Collaborative Action Research (CAR) approach, this project aims to assess the implementation of a telepresence robot, Double 3 to help residents connect with their families. CAR allows careful planning of implementation with stakeholders (patient and family partners, staff, and decision-makers), tailoring adaption to the complex LTC environment. We will program path planning to allow efficient movement between target destinations (residents' rooms) and the charging dock. For example, the robot will go to a resident’s room every morning or evening to help the resident to make a virtual call with family. The project involves three phases (a) Observe and Reflect, (b) Act and Adapt, (c) Evaluate. We work with two Canadian LTC homes in British Columbia to investigate feasibility and acceptability. CAR emphasizes research with, rather than research on people. Meaningful engagement with patient and family partners, frontline staff, and decision-makers at each site throughout the whole project will ensure the project will meet the local needs. Anticipated resident outcomes include improved quality of life, mood, perceived loneliness, perceived social support, and acceptance. Anticipated staff outcomes include perceived ease of use, and acceptability.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 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".