Evaluating the Construct Validity and Sensitivity to Change of the Klenico Depression Domain in Psychotherapeutic Inpatient Care: Instrument Validation Study
Notice bibliographique
Résumé
Background The accurate diagnosis of mental disorders, such as depression, requires comprehensive, valid, and reliable tools to ensure evidence-based treatments and effective outcome monitoring. Existing diagnostic practices often lack standardization, leading to missed comorbidities and variable diagnostic accuracy. The Klenico system is an innovative, web-based diagnostic tool that integrates patient self-reports with clinical validations by mental health professionals. This system covers a broad spectrum of mental disorders, including depression. Objective This research aimed to evaluate the psychometric properties of the Klenico Depression Domain (KDD), the component of the Klenico system that measures depressive symptomatology, in a real-world clinical setting. Specifically, the evaluation focused on the assessment of its construct validity, internal consistency, and sensitivity to change in symptom severity. Methods Anonymized data from 496 inpatients with mental disorders collected between 2019 and 2022 were analyzed. Patients completed the KDD alongside parts of the Patient Health Questionnaire (PHQ), Beck Depression Inventory (BDI-II), and Satisfaction With Life Scale (SWLS) at both admission and discharge. Internal consistency was measured using Cronbach α. Exploratory factor analysis was conducted to examine the factor structure. Construct validity was assessed via Pearson correlations with PHQ-9 and BDI-II, while divergent validity was tested against the PHQ Somatic Symptoms Scale (PHQ-15), PHQ–Generalized Anxiety Disorder-7, and SWLS. Sensitivity to change was evaluated using paired 1-tailed t tests, effect sizes, and repeated measures correlations. Results The KDD demonstrated excellent internal consistency (Cronbach α=0.91 at admission and 0.93 at discharge). Factor analysis revealed a 7-factor structure encompassing dimensions like “inadequacy,” “anhedonia,” and “self-hatred,” aligning with core depressive symptoms outlined in the International Statistical Classification of Diseases, Tenth Revision. The correlations with the convergent questionnaires PHQ-9 (r=0.68; P<.001) and BDI-II (r=0.70; P<.001) were high. While the KDD showed a moderate correlation with the divergent PHQ-15 (r=0.35; P<.001), it was more strongly associated with the divergent SWLS (r=–0.51; P<.001) and Generalized Anxiety Disorder-7 (r=0.51; P<.001). Sensitivity to change was high, with significant reductions in KDD scores for patients with improved symptoms (t27=5.36, P<.001; Cohen d=0.79) and high repeated measures correlation with both the BDI-II (r=0.61; P<.001) and the PHQ-9 (r=0.59; P<.001). Conclusions The KDD shows promise as a reliable and valid instrument for diagnosing depression and monitoring treatment outcomes in psychotherapeutic settings. Its alignment with International Statistical Classification of Diseases, Tenth Revision diagnostic criteria and sensitivity to symptom change underlines its potential utility. These findings highlight the Klenico system’s potential to enhance clinical diagnostics by addressing current gaps in mental health care, thus improving diagnostic accuracy and consistency. Further research is recommended to validate its performance across different populations and settings.
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 | 0,048 | 0,072 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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 ».