Evaluating the quality of work life of registered nurses in urban, rural and remote Northeastern Ontario
Bibliographic record
Abstract
The sustainability of our Canadian healthcare system to meet the demands of healthcare and healing for clients in urban, rural and remote hospital settings are dependent upon nurses’ health and the quality of nurses’ work life. The purpose of this research was to understand how Northeastern Ontario registered nurses’ (RNs’) in urban, rural and remote hospitals evaluated their quality of work life (QOWL), to examine similarities and differences of RNs’ evaluation related to their geographic locations, and to identify what QOWL and nursing practice environment factors were associated with nurses’ stress. A mixed methods sequential explanatory design was conducted that used an adapted version of the Nursing Work Life Model as the theoretical framework for Phase I. Quantitative data were collected from RNs working in urban, small urban, rural and remote Northeastern Ontario hospitals (n=4). The questionnaire consisted of demographic questions, the Brook’s Quality of Nursing Work Life, the Practice Environment Scale, the Nursing Stress scales (NSS), and a section for RNs to write comments. Multiple and logistic stepwise backward regressions were conducted to determine factors associated with nurses’ QOWL and stress scores. Phase II face-to-face interviews of RNs and nurse leaders (n=17) were conducted to explicate findings from Phase I results. Thematic analysis of participant comments (n=53), and semi-structured interviews were guided by Thorne’s (2008) Interpretative Description methods. A total of 319 packages were distributed and yielded a 54.23% response rate (n=173). The majority of RNs were female (93.1%) and ranged in age between 20 and 29 years (mean = 35.9, s.d. 11.0). Results from the QOWL multiple regression analysis indicated three key factors that explained 35% of the variance (R2 0.353) that included: general health, exhaustion, and factors in the staffing subscale of the Practice Environment Scale. Four key factors were associated with nurses’ stress scores that explained 42% of the variance (R2 0.423) and included: workload, work-home life balance, adequate support services, and factors of the nursing ability subscale of the Practice Environment Scale. Nurses who reported decreased presence of factors associated in the Nursing Quality subscale were 12.39 (95% CI: 2.58- 59.64) times as likely to have lower QOWL scores (≤163). Nurses who did not have adequate support services that allowed nurses to spend time with patients were 3.56 (95% CI: 1.78, 7.10) times as likely to report higher stress scores (≥ 78). The overarching theme summarizing the findings was revealed to be Supporting Holistic Client Healing and Nurse Healers that described nurses’ and nurse leaders’ evaluation of Northeastern Ontario nurses’ QOWL and stress. This was supported by five key themes: 1) Holistic Healing of Clients: Dueling Ideologies, 2) Facilitating Healing at the Bedside: Supporting Nurses’ Work Life, 3) Geographical Hindrances to Healing: Healthcare System Inequalities, 4) Supporting Healing Beyond the Hospital Bedside: Healthcare System Inequities in Policies, Funding and Decision-Making Processes, and 5) Nurses’ QOWL and Health Consequences. The findings of this research elucidated new knowledge related to factors impacting Northeastern Ontario nurses’ ability to provide quality holistic care to facilitate their clients healing processes, which affected nurses’ QOWL and stress. Supporting the holistic healing of clients and nurse healers requires nurses being able to access the supports and resources they need that maintains their legal and ethical standards of care. Inequitable healthcare system policies and decision-making processes that perpetuate healthcare system inequalities need to change. Improving nurses’ QOWL and stress requires a concerted effort by several stakeholders. Healthcare policies and decision-makers need to listen to the voices of nurses and healthcare providers who live and work in rural and remote settings across Northern Ontario. New and unique solutions and policies can be created that may eventually actualize the vision of the delivery of high quality healthcare services that are equitable for all Ontarians regardless of their geographic location.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".