Postsecondary Students' Information Needs and Pathways for Help with Stress, Anxiety, and Depression/Les Besoins D'information Des éTudiants Postsecondaires et Les Parcours Conçus Pour Réduire le Stress, L'anxiété, et la Dépression
Notice bibliographique
Résumé
Mental disorders are common among young adults, with the prevalence of the most common disorders, such as anxiety and depression, reaching a peak between ages 18 and 24 (Kessler, 2007). When considering postsecondary students more specifically-where academic, financial, and interpersonal stressors compound the age-related risk factors-the prevalence of mental disorders may be even higher (Cooke, Bewick, Barkham, Bradley, & Audin, 2006). One national U.S. survey found that almost half of the students sampled met the DSM-IV criteria for at least one mental disorder in the previous year, including 12% for an anxiety disorder and 18% for a mood disorder (Blanco et ah, 2008). More recently, a North American survey of students indicated that more than 80% of respondents felt exhausted and overwhelmed, with nearly half reporting that they felt hopeless at some point in the past academic year (American College Health Association, 2013). In line with these findings, rates of suicidality are also high among postsecondary students, with one U.S. national survey indicating that more than half of students surveyed had considered suicide at some point in their lives, including an alarming 8% of undergraduates who reported at least one suicide attempt (Drum, Brownson, Burton Denmark, & Smith, 2009).Given the high levels of mental health concerns among postsecondary students, one might expect that mental health service utilization would be similarly high; however, this is not the case (Kiley, 2013). A national U.S. survey showed that fewer than half the students screening positive for mood or anxiety disorders reported receiving any mental health services during the preceding year (Eisenberg, Golberstein, & Gollust, 2007). Although there have been apparent increases in the willingness of postsecondary students to access campus mental health services (Hunt & Eisenberg, 2010), young adults are among the age cohorts least likely to seek help for their mental health problems (Statistics Canada, 2011; Wang et ah, 2005). In addition, when young adults do seek treatment, they may not be basing their decisions on complete information or seeking this information from reliable sources. As such, it is worthwhile to explore the ways in which mental health information and treatment resources can be made more accessible for this vulnerable cohort.Much of the information that young adults want or need about mental health problems and treatment options can be understood within the framework of mental health literacy, which may be defined as the extent to which individuals in need of treatment are able to recognize and identify their symptoms as a condition requiring access to mental health resources (Coles & Coleman, 2010). The role of mental health literacy in service accessibility is highlighted by a recent Australian study that found only 26% of students would seek help from a general practitioner and only 10% from a student counsellor should they experience a mental health problem (Reavley, McCann, & Jorm, 2012). Results from a U.K. survey of 3,000 young people aged 16-24 showed similar results (Klineberg, Biddle, Donovan, & Gunnell, 2011). In this study, participants were asked to identify if characters from a vignette had depression, and what they thought the characters would do in terms of seeking help. Interestingly, about one third of the participants who recognized severe mental health symptoms in the vignettes thought that the characters would do nothing about their mental health problems (Klineberg et al., 2011).In addition to low rates of mental health literacy, previous research has identified a number of psychological factors that are related to reduced rates of help-seeking, including lack of emotional openness (Hunt & Eisenberg, 2010), degree of symptom severity (Leahy et al., 2010; Wilson, 2010), and self-stigma (Eisenberg, Downs, Golberstein, & Zivin, 2009). Of these factors, self-stigma is of particular concern because of its pervasive nature and impact. …
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 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,011 | 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 ».