Geographic availability to optometry services across Canada: mapping distribution, need and self-reported use
Notice bibliographique
Résumé
BACKGROUND: This research investigates the distribution of optometrists in Canada relative to population health needs and self-reported use of vision services. METHODS: Optometrist locations were gathered from provincial regulatory bodies. Optometrist-to-population ratios (i.e. the number of providers per 10,000 people at the health region level) were then calculated. Utilization of vision care services was extracted from the Canadian Community Health Survey (CCHS) 2013-2014 question regarding self-reported contacts with optometrists or ophthalmologists. Data from the 2016 Statistics Canada census were used to create three population 'need' subgroups (65 years and over; low-income; and people aged 15 and over with less than a high school diploma). Cross-classification mapping compared optometrist distribution to self-reported use of vision care services in relation to need. Each variable was converted into three classes (i.e., low, moderate, and high) using a standard deviation (SD) classification scheme where ±0.5SD from the mean was considered as a cut-off. Three classes: low (< - 0.5SD), moderate (- 0.5 to 0.5SD), and high (> 0.5SD) were used for demonstrating distribution of each variable across health regions. RESULTS: A total of 5959 optometrists across ten Canadian provinces were included in this analysis. The nationwide distribution of optometrists is variable across Canada; they are predominantly concentrated in urban areas. The national mean ratio of optometrists was 1.70 optometrists per 10,000 people (range = 0.13 to 2.92). Out of 109 health regions (HRs), 26 were classified as low ratios, 51 HRs were classified as moderate ratios, and 32 HRs were high ratios. Thirty-five HRs were classified as low utilization, 39 HRs were classified as moderate, and 32 HRs as high utilization. HRs with a low optometrist ratio relative to eye care utilization and a high proportion of key sociodemographic characteristics (e.g. older age, low income) are located throughout Canada and identified with maps indicating areas of likely greater need for optometry services. CONCLUSION: This research provides a nationwide overview of vision care provided by optometrists identifying gaps in geographic availability relative to "supply" and "need" factors. This examination of variation in accessibility to optometric services will be useful to inform workforce planning and policies.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».