| Literature DB >> 32726288 |
Golnar Aref-Adib1,2, Gabriella Landy3, Michelle Eskinazi1,2, Andrew Sommerlad1,2, Nicola Morant1, Sonia Johnson1,2, Richard Graham4, David Osborn1,2, Alexandra Pitman1,2.
Abstract
BACKGROUND: The use of digital technology can help people access information and provide support for their mental health problems, but it can also expose them to risk, such as bullying or prosuicide websites. It may be important to consider internet-related risk behavior (digital risk) within a generic psychiatric risk assessment, but no studies have explored the practice or acceptability of this among psychiatrists.Entities:
Keywords: internet; mental health; mixed methods; mobile phone; psychiatrists; risk assessment; self-injurious behavior; suicide
Year: 2020 PMID: 32726288 PMCID: PMC7424482 DOI: 10.2196/19008
Source DB: PubMed Journal: JMIR Ment Health ISSN: 2368-7959
Classification of digital risk.
| Risks | Content (person as recipient) | Contact (person as participant) | Conduct (person as initiator) |
| Commercial | Advertising, spam | Tracking/harvesting personal information | Gambling, illegal downloads, hacking |
| Aggressive | Violent/gruesome/hateful content | Being bullied, harassed, or stalked | Bullying or harassing another |
| Sexual | Pornographic/harmful sexual content | Meeting strangers, being groomed | Creating/uploading pornographic material |
| Values | Racist, biased information/advice (eg, drugs) | Self-harm, unwelcome persuasion/coercion | Providing advice (eg, suicide/proanorexia) |
Summary of demographic, professional backgrounds, and digital technology use.
| Characteristics | Values, n (%) | |
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| Male | 53 (46.9) |
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| Female | 60 (53.0) |
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| Core trainee | 60 (53.0) |
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| Higher trainee | 48 (42.5) |
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| Others | 5 (4.4) |
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| 0-5 | 64 (56.6) |
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| 5-10 | 40 35.4) |
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| ≥11 | 9 (8.0) |
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| Children and adolescents | 22 (19.5) |
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| Working age adults | 63 (55.8) |
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| Older adults | 11 (9.7) |
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| Forensic patients | 11 (9.7) |
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| Others | 7 (6.2) |
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| Possesses a smartphone | 105 (92.9) |
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| Possesses a tablet | 59 (52.2) |
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| Possesses a personal computer or laptop | 92 (81.4) |
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| Uses a phone/computer for more than an hour per day | 70 (61.9) |
Clinical experience and practice in relation to digital risk.
| Question asked, responses | All trainees | CAMHSa trainees | Other trainees (n=91), | Chi-square ( | |||
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| 8.2 (2) | .02 | |||||
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| Yes | 57 (50.4) | 17 (77) | 40 (44) |
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| No | 32 (28.3) | 2 (9) | 30 (33) |
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| Unsure | 24 (21.2) | 3(14) | 21 (23) |
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| 2.9 (1) | .10 | ||||
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| Yes | 87 (76.9) | 20 (91) | 67 (74) |
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| No | 26 (23.0) | 2(9) | 24 (26) |
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| 1.5 (1) | .30 | ||||
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| Yes | 99 (87.6) | 21 (95) | 78 (86) |
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| No | 14 (12.4) | 1 (5) | 13 (14) |
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| 4.2 (1) | .05 | ||||
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| Yes | 75 (72.8) | 19 (90) | 56 (68) |
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| No | 28 (27.2) | 2(10) | 26 (32) |
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| 4.5 (1) | .03 | ||||
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| Yes | 72 (70.6) | 18 (90) | 54 (66) |
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| No | 30 (29.4) | 2 (10) | 28 (34) |
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| 5.6 (1) | .01 | ||||
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| Yes | 77 (75.5) | 20 (95) | 57 (70) |
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| No | 25 (24.5) | 1 (5) | 24 (30) |
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| 3.1 (1) | .06 | ||||
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| Yes | 78 (75.7) | 19 (90) | 59 (72) |
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| No | 25 (24.3) | 2 (10) | 23 (28) |
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| 4.9 (1) | .02 | ||||
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| Yes | 61 (59.8) | 17 (81) | 44 (54) |
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| No | 41 (40.2) | 4 (19) | 37 (46) |
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| 0.3 (1) | .42 | ||||
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| Yes | 83 (81.4) | 18 (86) | 65 (80) |
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| No | 19 (18.6) | 3 (14) | 16 (20) |
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aCAMHS: child and adolescent mental health services.
bOn the basis of survey responses from 103 participants.
cOn the basis of survey responses from 102 participants.
Confidence and training in relation to digital risk.
| Question asked, responses | All trainees (n=113), | CAMHSa
| All other trainees | Chi-square ( | ||||
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| 0.8 (1) | .25 | ||||||
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| Yes | 37 (32.7) | 9 (41) | 28 (31) |
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| No | 76 (67.3) | 13 (59) | 63 (69) |
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| 2.2 (1) | .14 | ||||||
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| Yes | 11 (9.7) | 4 (18) | 7 (8) |
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| No | 102 (90.3) | 18 (82) | 84 (92) |
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| 0.86 (2) | .65 | ||||||
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| Yes | 83 (73.5) | 16 (73) | 67 (74) |
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| No | 6 (5.3) | 2 (9) | 4 (4) |
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| Unsure | 24 (21.2) | 4 (18) | 20 (22) |
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aCAMHS: child and adolescent mental health services.