Four Papers on the Nursing Labour Market in Ontario
Notice bibliographique
Résumé
This thesis examines issues pertaining to health human resources in the Ontario nursing profession, focusing on registered nurses (RNs) and registered practical nurses (RPNs). It consists of four chapters that explore the following nursing labour market trends: (1) multiple jobholding, part-time and casual employment, and other individual nurse and job-level characteristics (2) nursing job instability as measured by turnover and the number of years a job-worker (job-nurse) match exists, (3) nurse staffing agency employment, and (4) occupational attrition or turnover rates. The first two chapters compare nurses employed in the long-term care home (LTCH) sector, to the following healthcare sectors: hospitals, primary care, home care, supportive housing, public health (Chapter 2 only), and an aggregate “other” category. In Chapters 2 to 4, pre-COVID-19 trends are compared with the first, and where possible, the second year of the pandemic. All chapters in this thesis employ the Health Professions Database (HPDB), a dataset from the Ontario Ministry of Health, which derives from regulatory registration data. Chapter 1 examines the prevalence of multiple jobholding, part-time and casual employment, employment status versus employment preference, and other individual nurse and job level characteristics (e.g., the location of first education, languages spoken in practice). The results indicate the likelihood of multiple jobholding does not significantly differ in the LTCH sector compared to other healthcare sectors, especially among RPNs. Moreover, there is no evidence of excessive part-time and casual employment in LTCHs compared to other sectors. However, LTCH RNs and RPNs are significantly more likely to prefer full-time employment, while being employed in part-time or casual positions, referred to as involuntary part-time or casual status. Nurses are heterogenous across sectors in their individual characteristics and employment preferences. Notably, LTCH nurses are more likely to be internationally educated, and primary care nurses are more likely to prefer part-time employment. Chapter 2 investigates nursing job instability across the healthcare sectors found in Chapter 1, with the addition of public health. Average annual turnover (2014-2019) was 25.7 percent among LTCH RNs and 22.9 percent among LTCH RPNs. These findings demonstrate RN job turnover rates in LTCHs do not substantially deviate from those observed in other sectors and fall in the middle of the distribution. RPN job turnover rates in LTCHs are the second lowest observed, where turnover rates are lower in the hospital sector. Across both nurse categories, hospital jobs are the most durable, where a job-nurse match lasts 0.6 to 0.8 years longer than the average RN LTCH job, and 0.1 to 0.2 years longer than the average RPN LTCH job (over a five-year period). Results from 2020 indicate turnover increased the most in the LTCH and supportive housing sectors (by a maximum of 7.5 percent among LTCH RPNs) – the only two sectors where a single site restriction was implemented in 2020, making it difficult to interpret the cause of these findings. Chapter 3 documents the share of agency employed nurses, and the rate at which previously non-agency employed nurses obtain at least one agency position (the agency transition rate) over the 2011-2021 period. The results show that over the data period, the share of agency RNs was small (ranging from 2.4 to 3.4 percent), and slightly higher among RPNs (ranging from 6.1 to 7.1 percent). The agency transition rate is also low – ranging from 0.7 to 1.1 percent among RNs and 1.9 to 2.5 percent among RPNs from 2011-2021. The share of agency employment and the agency transition rate decreased during the first year of the pandemic (2020), and subsequently increased back to pre-pandemic levels in 2021. However, mean hours of work increased among agency (and non-agency) nurses, which may explain a small part of the increase in public spending on agency fees. Chapter 4 measures occupational turnover, where nurses leave the profession altogether, as opposed to job turnover (Chapter 2), which includes nurses who change jobs within the profession. Occupational turnover, or attrition rates, are lower compared to the job turnover rates found in Chapter 2. Annual attrition rates ranged from 6.1 to 7.2 percent among RNs and 6.6 to 7.5 percent among RPNs pre-pandemic (2014-2019). In the first two years of the pandemic, attrition rates increased modestly to 7.7 (2020) and 8.1 (2021) percent among RNs, and 8.0 (2020) and 8.6 percent (2021) among RPNs. Over the entire period of analysis, a larger share of attrition derives from nurses who register active, but are without Ontario nursing employment, compared to nurses who register inactive or do not register. Nurses who register active without Ontario nursing employment may be viewed as undertaking a more temporary exit, as such nurses are significantly more likely to return to the profession.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,008 | 0,032 |
| Études des sciences et des technologies | 0,007 | 0,002 |
| Communication savante | 0,007 | 0,003 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,028 | 0,002 |
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 ».