Evaluating the quality of work life of registered nurses in urban, rural and remote Northeastern Ontario
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».