Directed content analysis: A life course approach to understanding the impacts of the COVID-19 pandemic with implications for public health and social service policy
Notice bibliographique
Résumé
BACKGROUND: The COVID-19 pandemic has had broad impacts on individuals, families and communities which will continue to require multidimensional responses from service providers, program developers, and policy makers. OBJECTIVES: The purpose of this study was to use Life Course theory to understand and imagine public health and policy responses to the multiple and varied impacts of the COVID-19 pandemic on different groups. METHODS: "The Cost of COVID-19" was a research study carried out in Kingston, Frontenac, Lennox and Addington counties in South Eastern Ontario, Canada, between June and December 2020. Data included 210 micronarrative stories collected from community members, and 31 in-depth interviews with health and social service providers. Data were analyzed using directed content analysis to explore the fit between data and the constructs of Life Course theory. RESULTS: Social pathways were significantly disrupted by changes to education and employment, as well as changes to roles which further altered anticipated pathways. Transitions were by and large missed, creating a sense of loss. While some respondents articulated positive turning points, most of the turning points reported were negative, including fundamental changes to relationships, family structure, education, and employment with lifelong implications. Participants' trajectories varied based on principles including when they occurred in their lifespan, the amount of agency they felt or did not feel over circumstances, where they lived (rural versus urban), what else was going on in their lives at the time the pandemic struck, how their lives were connected with others, as well as how the pandemic impacted the lives of those dear to them. An additional principle, that of Culture, was felt to be missing from the Life Course theory as currently outlined. CONCLUSIONS: A Life Course analysis may improve our understanding of the multidimensional long-term impacts of the COVID-19 pandemic and associated public health countermeasures. This analysis could help us to anticipate services that will require development, training, and funding to support the recovery of those who have been particularly affected. Resources needed will include education, mental health and job creation supports, as well as programs that support the development of individual and community agency.
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 | 0,025 | 0,038 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,014 | 0,011 |
| Études des sciences et des technologies | 0,009 | 0,012 |
| Communication savante | 0,012 | 0,010 |
| Science ouverte | 0,004 | 0,007 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
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.
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 ».