Deployment experiences of military nurses: A systematic review and qualitative meta‐synthesis
Bibliographic record
Abstract
AIMS: The purpose of this systematic review is to explore military nurses' preparation, deployment and reintegration experiences in order to provide recommendations for effective management of the nursing team. BACKGROUND: Nurses provide health care in different settings including community, hospital and the disaster site. Military nurses have a long history of deploying for global health. METHOD: A systematic review and qualitative meta-synthesis of studies focusing on the preparation, deployment and reintegration experiences of military nurses was carried out. RESULTS: Five synthesized findings were concluded: (a) preparing and sharing experience are the key coping strategies; (b) transition from the civilian care to emergency situations; (c) teamwork contributing to team bonding and the growing role of nursing in the medical team; (d) devoting to nursing duty achieves growth; (e) reintegration is not easy and external support matters. CONCLUSION: Transition from civilian care to deployment and from structured deployment environment to reintegration poses challenges to nurses, and better preparation, sufficient support enables them to gain growth. IMPLICATIONS FOR NURSING MANAGEMENT: Nurse managers should consider how to sustain a competent and ready nursing team by proposing training protocols to nurses for the potential challenges during the deployment cycle when responding to disasters and public emergencies.
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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 | 0.021 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".