Bridging Educational Grant in Nursing (BEGIN) students’ intentions for retention in long-term, home and community care: A survey protocol
Bibliographic record
Abstract
INTRODUCTION: Retention of nurses in long-term care (LTC) and home and community care (HCC) settings is a growing concern. Previous evidence underscores factors which contribute to nurses' intentions for retention in these sectors. However, perspectives of nursing students preparing to enter the workforce, and their intentions for short-term and long-term retention, remain unknown. This study aims to explore relationships between short-term and long-term intentions for retention with psychological empowerment, work engagement, career commitment, burnout, prosocial motivation, self-care and personal resilience among students enrolled in nursing educational bridging programs supported by the Bridging Educational Grant in Nursing (BEGIN) program in Ontario, Canada. METHODS AND ANALYSIS: This cross-sectional design study will use an open online survey to investigate perspectives of current nursing students enrolled in educational bridging programs on factors relating to psychological empowerment, work engagement, career commitment, resilience, burnout, prosocial motivation, self-care and intentions for retention. Additionally, the survey will collect demographic information, including age, gender, ethnicity, citizenship, income, family status, nursing role, and years of employment and/or education. Open-ended questions will elicit participants' perspectives on financial considerations for career planning and other factors impacting intentions for retention. Descriptive data will be presented for contextualisation of participants' demographic characteristics to enhance generalisability of the cohort. Descriptive statistics will be used to summarise participants' scores on various assessment measures, as well as their short-term and long-term intentions for retention in LTC and HCC after completion of BEGIN. A Pearson's product moment r correlation will determine relationships between intentions for retention and other measures, and linear regression will determine whether any potential correlations can be explained by regression. ETHICS AND DISSEMINATION: This research protocol received ethical approval from a research-intensive university research ethics board (#123211). Findings will be disseminated to nursing knowledge users in LTC and HCC through publications, conferences, social media and newsletters.
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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 | 0.037 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 0.007 |
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".