A Multi‐Method Study of the Geriatric Learning Needs of Acute Care Hospital Nurses in Ontario, Canada
Bibliographic record
Abstract
Older people are at risk of experiencing functional decline and related complications during hospitalization. In countries with projected increases in age demographics, preventing these adverse consequences is a priority. Because most Canadian nurses have received little geriatrics content in their basic education, understanding their learning needs is fundamental to preparing them to respond to this priority. This two-phased multi-method study identified the geriatrics learning needs and strategies to address the learning needs of acute care registered nurses (RNs) and registered practical nurses (RPNs) in the province of Ontario, Canada. In Phase I, a survey that included a geriatric nursing knowledge scale was completed by a random sample of 2005 Ontario RNs and RPNs. Average scores on the geriatric nursing knowledge scale were in the "neither good nor bad" range, with RNs demonstrating slightly higher scores than RPNs. In Phase II, 33 RN and 24 RPN survey respondents participated in 13 focus group interviews to help confirm and expand survey findings. In thematic analysis, three major themes were identified that were the same in RNs and RPNs: (a) geriatric nursing is generally regarded as simple and custodial, (b) older people's care is more complex than is generally appreciated, and (c) in the current context, older people's care is best learned experientially and in brief on-site educational sessions. Healthcare providers, policy-makers, and educators can use the findings to develop educational initiatives to prepare RNs and RPNs to respond to the needs of an aging hospital population.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".