User characteristics and outcomes from a national digital mental health service: an observational study of registrants of the Australian MindSpot Clinic
Notice bibliographique
Résumé
Background: Interest is growing in digital and telehealth delivery of mental health services, but data are scarce on outcomes in routine care. The federally funded Australian MindSpot Clinic provides online and telephone psychological assessment and treatment services to Australian adults. We aimed to summarise demographic characteristics and treatment outcomes of patients registered with MindSpot over the first 7 years of clinic operation. Methods: We used an observational design to review all patients who registered for assessment with the MindSpot Clinic between Jan 1, 2013, and Dec 31, 2019. We descriptively analysed the demographics, service preferences, and baseline symptoms of patients. Among patients enrolled in a digital treatment course, we evaluated scales of depression (Patient Health Questionnaire-9 [PHQ-9]) and anxiety (Generalized Anxiety Disorder 7-Item Scale [GAD-7]), as primary measures of treatment outcome, from the screening assessment to post-treatment and a 3 month follow-up. The Kessler Psychological Distress 10-Item Plus Scale was also used to assess changes in general distress and disability, and course satisfaction was measured post-treatment. Outcomes: A total of 121 652 screening assessments were started, of which 96 018 (78·9%) were completed. The mean age of patients was 35·7 years (SD 13·8) and 88 702 (72·9%) were women. Based on available assessment data, 36 866 (34·5%) of 106 811 participants had never previously spoken to a health professional about their symptoms, and most people self-reported symptoms of anxiety (88 879 [81·9%] of 108 494) or depression (78 803 [72·6%] of 108 494), either alone or in combination, at baseline. 21 745 patients started treatment in a therapist-guided online course, of whom 14 503 (66·7%) completed treatment (≥four of five lessons). Key trends in service use included an increase in the proportion of people using MindSpot primarily for assessment and information, from 52·6% in 2013 to 66·7% in 2019, while the proportion primarily seeking online treatment decreased, from 42·6% in 2013 to 26·7% in 2019. Effect sizes and percentage changes were large for estimated mean scores on the PHQ-9 and GAD-7 from assessment to post-treatment (PHQ-9, Cohen's d effect size 1·40 [95% CI 1·37-1·43]; and GAD-7, 1·45 [1·42-1·47]) and the 3 month follow-up (PHQ-9, 1·36 [1·34-1·38]; and GAD-7, 1·42 [1·40-1·44]); proportions of patients with reliable symptom deterioration (score increase of ≥6 points [PHQ-9] or ≥5 points [GAD-7]) were low post-treatment (of 13 058 respondents, 184 [1·4%] had symptom deterioration on the PHQ-9 and 282 [2·2%] on the GAD-7); and patient satisfaction rates were high (12 452 [96·6%] of 12 895 respondents would recommend the course and 12 433 [96·7%] of 12 860 reported the course worthwhile). We also observed small improvements in disability following treatment as measured by days out of role. Interpretation: Our findings indicate improvement in psychological symptoms and positive reception among patients receiving online mental health treatment. These results support the addition of digital services such as MindSpot as a component in contemporary national mental health systems. Funding: None.
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 | 0,002 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».