The meaning of nursing practice for nurses who are retired yet continue to work in a rural or remote community
Bibliographic record
Abstract
BACKGROUND: Although much research has focused on nurses' retirement intentions, little is known about nurses who formally retire yet continue to practice, particularly in rural and remote settings where mobilization of all nurses is needed to assure essential health services. To optimize practice and sustain the workforce stretched thin by the COVID-19 pandemic, it is necessary to understand what it means for retired registered nurses (RNs) and licensed practical nurses (LPNs) to work after retirement. This study explored what nursing practice means for RNs and LPNs who have formally retired but continue to practice in rural and remote communities. METHODS: A pan-Canadian cross-sectional survey conducted in 2014-2015 of nurses in rural and remote Canada provided data for analysis. Textual responses from 82 RNs and 19 LPNs who indicated they had retired but were occasionally employed in nursing were interpreted hermeneutically. RESULTS: Retired nurses who continued to practice took on new challenges as well as sought opportunities to continue to learn, grow, and give back. Worklife flexibility was important, including having control over working hours. Nurses' everyday practice was inextricably tied up with their lives in rural and remote communities, with RNs emphasizing serving their communities and LPNs appreciating community recognition and the family-like character of their work settings. CONCLUSIONS: Retired nurses who continue to work in nursing see retirement as the next phase in their profession and a vital way of engaging with their rural and remote communities. This study counters the conventional view of retaining retired nurses only to combat nursing shortages and alleviate a knowledge drain from the workplace. Rural and remote nurses who retire and continue working contribute to their workplaces and communities in important and innovative ways. They can be characterized as dedicated, independent, and resilient. Transitioning to retirement in rural and remote practice can be re-imagined in ways that involve both the community and the workplace. Supporting work flexibility for retired nurses while facilitating their practice, technological acumen, and professional development, can allow retired nurses to contribute their joy of being a nurse along with their extensive knowledge and in-depth experience of nursing and the community.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.032 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".