Effective Retention Strategies for Midcareer Critical Care Nurses
Bibliographic record
Abstract
BACKGROUND: Midcareer nurses continue to be overlooked in the current nursing shortage that is amplified in intensive care units (ICUs) requiring greater numbers of specialized nurses. OBJECTIVES: The aim of this study was to discover what midcareer critical care nurses perceive would be effective retention strategies. METHODS: As a combination of both qualitative and quantitative approaches, Q methodology was used to allow for the development of innovative strategies as well as to provide an understanding of a population of viewpoints and preferences that can guide retention efforts. Forty ICU nurses between the ages of 25 and 44 years from within a Canadian academic health science corporation completed a 45-item Q sort representing their ideas for increasing staff retention. Data were analyzed using centroid factor extraction and varimax rotation in PQMethod version 2.11. RESULTS: Four viewpoints emerged: The Healthy Workplace and Respect Seeker, The Flexibility and Reward Seeker, The Professional Development and Teamwork Seeker, and The Lifestyle Seeker. Correlations between the factors were appropriately weak, with seemingly distinct demographics characterizing each. DISCUSSION: These findings suggest a possible association between perceptions and both years of nursing experience as well as age. Implications from the study include the need to involve frontline nurses in developing strategies that will retain them. Following further investigation of the nurses' preferred strategies, it may be necessary for organizations to develop an array of retention strategies rather than implementing a single solution. In future research, generational preferences and the possible dissonance between nurse managers and frontline nurses' perceptions should be explored.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".