| Literature DB >> 34873845 |
Laura Orsolini1, Chonnakarn Jatchavala2, Isa Multazam Noor3, Ramdas Ransing4, Yuto Satake5, Sheikh Shoib6, Bigya Shah7, Irfan Ullah8, Umberto Volpe1.
Abstract
BACKGROUND: Digital mental health interventions and digital psychiatry have been rapidly implemented over the past decade, particularly with the intent to offer a cost-effective solution in those circumstances in which the current mental health services and infrastructure are not able to properly accommodate the patients' needs. However, mental health workforce is often poorly theoretical/practical trained in digital psychiatry and in delivering remote consultations safely and effectively, not being common to own curricula-specific training requirements in digital psychiatry and skills.Entities:
Keywords: Asia-Pacific region; digital psychiatry; e-mental health; medical education; psychiatry training; telemedicine; telepsychiatry
Mesh:
Year: 2021 PMID: 34873845 PMCID: PMC9285069 DOI: 10.1111/appy.12501
Source DB: PubMed Journal: Asia Pac Psychiatry ISSN: 1758-5864 Impact factor: 3.788
Sociodemographic characteristics of the sample
|
| ||
|---|---|---|
| Gender | Male | 123 (64.1%) |
| Female | 69 (35.9%) | |
| Marital status | Single (never married) | 130 (67.7%) |
| Married or co‐living partner | 38 (19.8%) | |
| In a stable affective relationship | 24 (12.5%) | |
| Country of residency | Indonesia | 5 (2.6%) |
| Japan | 13 (6.8%) | |
| Pakistan | 17 (8.9%) | |
| Thailand | 38 (19.8%) | |
| Nepal | 59 (30.7%) | |
| India | 60 (31.3%) | |
| Living city | Village/rural | 17 (8.9%) |
| Small city/town (10 000–100 000 population) | 23 (12%) | |
| Medium city/town (100 000–500 000 population) | 59 (30.7%) | |
| Large city/town (over 500 000 population) | 93 (48.4%) | |
| Born country | South Korea | 1 (0.5%) |
| Indonesia | 6 (3.1%) | |
| Japan | 12 (6.3%) | |
| Pakistan | 17 (8.9%) | |
| Thailand | 38 (19.8%) | |
| Nepal | 57 (29.7%) | |
| India | 61 (31.8%) | |
| Ethnicity | Caucasian | 5 (2.6%) |
| Asian | 172 (89.6%) | |
| Mixed | 15 (7.8%) | |
| World Bank Income | Low | 30 (15.6%) |
| Lower‐middle | 92 (47.9%) | |
| Upper‐middle | 61 (31.8%) | |
| High | 9 (4.7%) | |
| Current academic role | Medical students | 104 (54.2%) |
| Medical doctors waiting for starting psychiatry training program | 10 (5.2%) | |
| Psychiatry trainees | 37 (19.3%) | |
| Early career psychiatrists | 41 (21.4%) | |
| Country of medical college | China | 2 (1%) |
| Bangladesh | 2 (1%) | |
| Australia | 2 (1%) | |
| Indonesia | 6 (3.1%) | |
| Japan | 13 (6.8%) | |
| Pakistan | 20 (10.4%) | |
| Thailand | 40 (20.8%) | |
| Nepal | 52 (27.1%) | |
| India | 55 (28.6%) | |
| Country of psychiatry residency | USA | 1 (0.5%) |
| Australia | 2 (1%) | |
| Indonesia | 6 (3.1%) | |
| Japan | 13 (6.8%) | |
| Thailand | 38 (19.8%) | |
| Nepal | 48 (25%) | |
| India | 59 (30.7%) | |
FIGURE 1Knowledge scores across countries of residency
FIGURE 2Knowledge scores across countries, according to the World Bank Income
FIGURE 3Knowledge scores according to academic role/position