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Record W7106006135 · doi:10.7939/83121

Nurse Leader Experiences with Supporting the Return to Work (RTW) of Nurses Following Operational Stress Injury (OSI)

2025· dissertation· en· W7106006135 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of Alberta Library · 2025
Typedissertation
Languageen
FieldPsychology
TopicPosttraumatic Stress Disorder Research
Canadian institutionsnot available
Fundersnot available
KeywordsQualitative researchInclusion (mineral)Health careInterpersonal communicationWork (physics)Nurse managerInterpersonal relationshipOccupational stressOccupational safety and healthTraumatic stress

Abstract

fetched live from OpenAlex

Background: Nurses are exposed to potentially psychologically traumatic events in the workplace which can lead to operational stress injuries (OSIs). Workplace reintegration after an OSI can be challenging, especially with repeated exposure to potentially traumatic scenarios, returning to the site of their initial injury, and workplace demands. Nurse leaders, such as unit managers, patient care managers, and clinical nurse educators, have the unique role of assisting OSI-injured nurses with their reintegration into the workplace after injury. Objective: This study aims to examine nurse leaders’ experiences on the return-to-work (RTW) process for nurses recovering from OSIs, and to identify barriers and opportunities to improve reintegration practices in healthcare settings. Theoretical Framework: This study adopts a systems-level perspective, drawing on organizational behavior and trauma-informed care theories to explore individual, managerial, and organizational influences on RTW experiences. Methods: This thesis used qualitative descriptive analysis to explore the RTW processes of nurses. It consists of both a (1) literature review that explored the current state of the literature, and (2) qualitative study that used semi-structured interviews conducted with nursing leaders across Alberta, Canada to capture leadership perspectives. Results: Four articles meeting inclusion criteria and transcripts from 12 interviewed nurse leaders were thematically analyzed to identify issues impacting RTW across micro, meso, and macro levels. Based on the literature, effective support relies on a combination of organizational structure, interpersonal skills of the RTW coordinator, case management expertise, and flexibility. Three key themes emerged from the interviews: 1) micro level: psychological impacts for the individual, 2) meso level - leadership approaches to RTW and deficiencies, and 3) macro level - organizational structure and workplace culture inclusive of challenges such as the invisibility of psychological injuries, leadership gaps, and systemic cultural barriers to effective reintegration of OSIs. Conclusions: The study revealed critical deficiencies in current RTW practices in nurses with OSI’s, underscoring the need for trauma-informed leadership, integrated mental health support, adequate leadership training and organizational cultures that prioritize psychological well-being. Addressing these gaps is essential for improving retention, recovery outcomes, and workplace health.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0060.003
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.019
GPT teacher head0.310
Teacher spread0.291 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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