WORKPLACE RETENTION FRAMEWORKS FOR NURSING IN LONG-TERM CARE: AN INTEGRATIVE REVIEW
Bibliographic record
Abstract
Abstract Background Nurses and Personal Support Workers are essential for care and services provided for Long term Care (LTC) homes. Concerns regarding unsafe living and working condition resulting in high turnover rates. Despite high turnover rate, very little guidance to help improve staffing stability and workforce retention planning. The purpose of this integrative review is to synthesise the existing literature regarding LTC staff retention to guide the development of a comprehensive framework addressing LTC staffing issues. Methods This review was conducted using Whittemore and Knafl’s integrative review methodology to assess both empirical, theoretical and grey literature. Databases were searched such as CIHNL, PubMed, PyschINFO. Included articles are published in English; use empirical and theoretical methodologies; and focuses on workplace retention frameworks, models, and strategies for the LTC workforce. Articles involving non-facility based LTC and those focused on recruitment rather than retention was excluded. Thematic analysis using Braun & Clark was used to identify themes related to retention frameworks and models influencing turnover. Results A total of 1196 articles were retrieved, with 1086 articles screened, while 17 articles were included in the final sample. Examples of identified models including Huber’s Structural Model and the Dual-Driver Model. Retention framework focuses on five group factors: Organizational and personal characteristics, leadership/management, social systems and labour market conditions. Conclusion The results of this study will be useful to guide subsequent stages of this research to gain consensus of LTC staff using Delphi methodology to support the co-creation of a workforce retention framework in LTC sector.
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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 | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".