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Enregistrement W6906369429 · doi:10.17605/osf.io/z5tkp

Examining the impact of healthcare sector Covid-19 policies on access to care in Ontario, Canada: A mixed methods study

2023· other· en· W6906369429 sur OpenAlexaboutno aff

Notice bibliographique

RevueOpen Science Framework · 2023
Typeother
Langueen
DomaineSocial Sciences
ThématiqueVaccine Coverage and Hesitancy
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésGovernment (linguistics)VaccinationHealth careHarmAuthorizationReimbursementVaccine safetyPatient safety

Résumé

récupéré en direct d'OpenAlex

Statement of the problem: In December of 2020, and upon an FDA Emergency Use Authorization of two mRNA-Covid-19 vaccines, a vaccination campaign was launched in most hospitals across Canada. Vaccine products were tested for safety and efficacy in large, albeit short-term, RCTs, that included mostly young and healthy adults. Potential safety signals, many of them long term, would become apparent over time – through data from early adopters such as Israel and India, or reporting systems across the world, such as the United Kingdom (UK) Yellow Card, the World Health Organization VigiBase, or the United States (US) Centers for Disease Control (CDC) Vaccine Adverse Events Reporting System (VAERS). These signals were concerning. For instance, already in January 2021, VAERS would record a higher number of deaths post-Covid vaccination than any other vaccine since the system was established in 1990. By mid-2021, the number of recorded deaths post-Covid-19 vaccination would surpass the total number of deaths post all vaccines deployed in the preceding 30 years. Many healthcare workers with firsthand experience in their day-to-day practice of vaccine harms on patients or colleagues, and/or access to data from Israel, India, or systems such as VAERS, were cognizant of signals of harm not included in the hospital-provided vaccine information literature. Anecdotal evidence indicates that as a result, many chose not to get vaccinated despite job loss being the cost of such choice. Many others opted for early retirement or career change. Yet others became disabled or lost their lives, as indicated by government and actuarial data from the UK and the USA. The type and scope of the impact of the policy of vaccine mandates on the Canadian health workforce, however, remains unknown. Given the ongoing healthcare labor force shortage affecting Canadians, documenting, and explaining what one major contributor may be is critical. Our project assesses this potential contributor through a mixed-method study of the province of Ontario, Canada. Research question: Our main research question is: “What has been the impact of healthcare sector Covid-19 policies on access to care in Ontario, Canada?” Ancillary questions include: “What is known from publicly accessible governmental data (e.g., Ontario Health’s databases) and Freedom of Information Requests (FOIR) from government and hospitals, about the impact of the Covid-19 vaccine mandates on the healthcare labour force, specifically staff reductions? What is known about the implications of this impact for access to care in the province? What is the rationale informing the current policy of ongoing vaccine mandates for new and existing staff? What are the views and lived experience of Canadian health workers concerning the crisis in the health labour force? Aims: Our study aims to document and explain an under researched aspect of the drivers of the health crisis, specifically the impact of healthcare sector Covid-19 policies, on access to care in Ontario. Methods: To address our research questions and achieve our study aim, we will use Creswell (2015) mixed methods approach to inform our narrative literature review, policy analysis, FOIA investigation, survey of healthcare workers, and interview of a sample of workers from within survey respondents. Significance: The Covid-19 crisis has resulted in a severe understaffing of hospitals across Ontario, compounding the problems of lack of experienced health workers and staff burnout. To the best of our knowledge, there is no research into what led to the policy decision to terminate experienced, healthy unvaccinated health workers, a decision ongoing to this day. By summarizing extant evidence, identifying information gaps, surveying the health workforce, and gaining a deeper understanding of the lived experience within this workforce our research should contribute to more effective and equitable policies in the health sector moving forward, relevant not only to the province of Ontario but potentially throughout Canada.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,012
score de la tête « metaresearch » (Gemma)0,033
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,163
Score d'incertitude au seuil0,971

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0120,033
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0020,003
Bibliométrie0,0040,011
Études des sciences et des technologies0,0090,003
Communication savante0,0050,002
Science ouverte0,0060,003
Intégrité de la recherche0,0020,002
Charge utile insuffisante (le modèle a refusé de juger)0,0070,001

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.

Tête enseignante Opus0,158
Tête enseignante GPT0,507
Écart entre enseignants0,350 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2023
Routes d'admission1
Résumé présentoui

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