An Internet Resource for Self-Assessment of Mental Health and Health Behavior: Development and Implementation of the Self-Assessment Kiosk
Notice bibliographique
Résumé
BACKGROUND: Standardized measurement of physical and mental health is useful for identification of health problems. Personalized feedback of the results can influence health behavior, and treatment outcomes can be improved by monitoring feedback over time. However, few resources are available that are free for users, provide feedback from validated measurement instruments, and measure a wide range of health domains. OBJECTIVE: This study aimed to develop an internet self-assessment resource that fills the identified gap and collects data to generate and test hypotheses about health, to test its feasibility, and to describe the characteristics of its users. METHODS: The Self-Assessment Kiosk was built using validated health measurement instruments and implemented on a commercial internet survey platform. Data regarding usage and the characteristics of users were collected over 54 weeks. The rate of accrual of new users, popularity of measurement domains, frequency with which multiple domains were selected for measurement, and characteristics of users who chose particular questionnaires were assessed. RESULTS: Of the 1435 visits, 441 (30.73%) were visiting for the first time, completed at least 1 measure, indicated that their responses were truthful, and consented to research. Growth in the number of users over time was approximately linear. Users were skewed toward old age and higher income and education. Most (53.9%, 234/434) reported at least 1 medical condition. The median number of questionnaires completed was 5. Internal reliability of most measures was good (Cronbach alpha>.70), with lower reliability for some subscales of coping (self-distraction alpha=.35, venting alpha=.50, acceptance alpha=.51) and personality (agreeableness alpha=.46, openness alpha=.45). The popular questionnaires measured depression (61.0%, 269/441), anxiety (60.5%, 267/441), attachment insecurity (54.2%, 239/441), and coping (46.0%, 203/441). Demographic characteristics somewhat influenced choice of instruments, accounting for <9% of the variance in this choice. Mean depression and anxiety scores were intermediate between previously studied populations with and without mental illness. Modeling to estimate the sample size required to study relationships between variables suggested that the accrual of users required to study the relationship between 3 variables was 2 to 3 times greater than that required to study a single variable. CONCLUSIONS: The value of the Self-Assessment Kiosk to users and the feasibility of providing this resource are supported by the steady accumulation of new users over time. The Self-Assessment Kiosk database can be interrogated to understand the relationships between health variables. Users who select particular instruments tend to have scores that are higher than those found in the general population, indicating that instruments are more likely to be selected when they are salient. Self-selection bias limits generalizability and needs to be taken into account when using the Self-Assessment Kiosk database for research. Ethical issues that were considered in developing and implementing the Self-Assessment Kiosk are discussed.
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 | 0,008 | 0,019 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,004 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,019 | 0,012 |
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 ».