How do practically trained (student) caregivers in nursing homes learn? A scoping review
Bibliographic record
Abstract
Background and objective: Practically trained (student) caregivers (further: caregivers) make up the majority of care staff in nursing homes (NHs). To keep up with the fast-changing healthcare environment and ensure a high quality of care, it is important to know how to stimulate continuous work-based learning (WBL) among this group. The purpose of the study was to systematically study the scientific literature published to date on (1) how caregivers learn in NHs and (2) what facilitates or impedes their learning.Methods: A scoping review was carried out, systematically searching six scientific databases. A total of 35 studies published from January 2009 to February 2021 were included. Study characteristics, learning mechanisms, facilitators, and barriers to learning were extracted and synthesized.Results: None of the studies specifically focused on how caregivers learn. Yet, we identified various learning mechanisms, and found that learning by theory or supervision was most frequently engaged in. Most learning mechanisms used among the groups in the included studies were planned and formal and developed and initiated by others out of the context. Three main themes were identified among the facilitators and barriers of WBL: individual learning, collective learning, and resources for learning. An interdependency between (sub)themes was found.Conclusions: The way caregivers in NHs learn is understudied. Moreover, both their informal learning and the support they receive to be(come) active learners has been overlooked. As WBL provides caregivers with opportunities to learn within a real-life setting, we suggest more research on informal learning mechanisms.
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 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.016 | 0.018 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".