The Association Between new Nurses’ Gerontological Education, Personal Attitudes Toward Older Adults, and Intentions to Work in Gerontological Care Settings in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: The older adult population in Canada is increasing, and many will require care within an acute geriatric unit (AGU) or long-term care facility (LTCF). However, the nursing workforce is not growing at the same pace as the population is aging. New graduate nurses may be able to fill this gap; therefore, it is important to understand their intentions of working in gerontological care settings (i.e., AGU or LTCF). AIM: To examine if nursing education and personal attitudes toward older adults influence newly registered nurses'(RNs) intentions to work in a gerontological care setting. METHOD: Nurses (n= 1,103) who registered with the College of Nurses of Ontario for the first time in 2018 were invited to complete a questionnaire. RESULTS: The majority of participants (n = 181) reported a positive attitude toward older adults. However, only 14% reported an intention to work in a gerontological care setting. Participants who completed multiple geriatric focused clinical placements were more likely to report an intention to work in these settings. CONCLUSION: This study provides some information regarding the attitudes and intentions of newly RNs toward a career in gerontological care settings. Further research is needed to understand nurses' intentions regarding working in AGUs or LTCFs.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".