Impact of COVID-19 on healthcare access for Australian adolescents and young adults
Notice bibliographique
Résumé
ABSTRACT Background Access to healthcare for young people is essential to build the foundation for a healthy life. We investigated the factors associated with healthcare access by Australian young adults during and before the COVID-19 pandemic. Methods We included 1110 youths using two recent data collection waves from the Longitudinal Study of Australian Children (LSAC). Data were collected during COVID-19 in 2020 for Wave 9C1 and before COVID-19 in 2018 for Wave 8. The primary outcome for this study was healthcare access. Both bivariate and multivariate logistic regression models were employed to identify the factors associated with reluctance to access healthcare services during COVID-19 and pre-COVID-19 times. Results Among respondents, 39.6% avoided seeking health services during the first year of the COVID-19 pandemic when they needed them, which was similar to pre-COVID-19 times (41.4%). The factors most strongly impacting upon reluctance and/or barriers to healthcare access during COVID-19 were any illness or disability, and high psychological distress. In comparison, prior to the pandemic the factors which were significantly impeding healthcare access were country of birth, state of residence, presence of any pre-existing condition and psychological distress. The most common reason reported (55.9%) for avoided seeking care was that they thought the problem would go away. Conclusions A significant proportion of youths did not seek care when they felt they needed to seek care, both during and before the COVID-19 pandemic. What is known about the subject? Some adolescents and young adults do not access healthcare when they need it. Healthcare access and barriers to access is best understood through a multi-system lens including policy, organisational, and individual-level factors. For instance, policy barriers (such as cost), organisational barriers (such as transportation, or difficulty accessing a timely appointment) and individual barriers (such as experiences, knowledge or beliefs). Barriers to care may differ for sub-groups e.g. rural During the COVID-19 pandemic, public health restrictions including the stricter “lockdowns” have reduced healthcare access. The burden of cases upon the healthcare system has further reduced healthcare access. What this study adds? A significant proportion of youth did not seek healthcare when they felt they needed to seek care, both before (41.4%) and during the first year of the COVID-19 pandemic (39.6%) Youth with a disability or chronic condition, asthma and/or psychological distress were more likely to avoid accessing healthcare during COVID-19 times. The most common reason for not seeking healthcare when it was felt to be needed was because the youth thought the problem would go away (pre-COVID-19 35.7% of the sample versus during the first year of COVID-19 55.9%) During the coronavirus restriction period (“lockdown”) the most common reason for not seeking healthcare when it was felt to be needed was because the youth did not want to visit a doctor during lockdown (21.8%) with the next most common reason being because telehealth was the only appointment option available at the time (8.4%)
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 | 0,001 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».