Brief Digital Interventions to Support the Psychological Well-being of NHS Staff During the COVID-19 Pandemic: 3-Arm Pilot Randomized Controlled Trial
Notice bibliographique
Résumé
BACKGROUND: Health and social care staff are at high risk of experiencing adverse mental health (MH) outcomes during the COVID-19 pandemic. Hence, there is a need to prioritize and identify ways to effectively support their psychological well-being (PWB). Compared to traditional psychological interventions, digital psychological interventions are cost-effective treatment options that allow for large-scale dissemination and transcend social distancing, overcome rurality, and minimize clinician time. OBJECTIVE: This study reports MH outcomes of a Consolidated Standards of Reporting Trials (CONSORT)-compliant parallel-arm pilot randomized controlled trial (RCT) examining the potential usefulness of an existing and a novel digital psychological intervention aimed at supporting psychological health among National Health Service (NHS) staff working through the COVID-19 pandemic. METHODS: NHS Highland (NHSH) frontline staff volunteers (N=169) were randomly assigned to the newly developed NHSH Staff Wellbeing Project (NHSWBP), an established digital intervention (My Possible Self [MPS]), or a waitlist (WL) group for 4 weeks. Attempts were made to blind participants to which digital intervention they were allocated. The interventions were fully automated, without any human input or guidance. We measured 5 self-reported psychological outcomes over 3 time points: before (baseline), in the middle of (after 2 weeks), and after treatment (4 weeks). The primary outcomes were anxiety (7-item General Anxiety Disorder), depression (Patient Health Questionnaire), and mental well-being (Warwick-Edinburgh Mental Well-being Scale). The secondary outcomes included mental toughness (Mental Toughness Index) and gratitude (Gratitude Questionnaire-6). RESULTS: Retention rates mid- and postintervention were 77% (n=130) and 63.3% (n=107), respectively. Postintervention, small differences were noted between the WL and the 2 treatment groups on anxiety (vs MPS: Cohen d=0.07, 95% CI -0.20 to 0.33; vs NHSWBP: Cohen d=0.06, 95% CI -0.19 to 0.31), depression (vs MPS: Cohen d=0.37, 95% CI 0.07-0.66; vs NHSWBP: Cohen d=0.18, 95% CI -0.11 to 0.46), and mental well-being (vs MPS: Cohen d=-0.04, 95% CI -0.62 to -0.08; vs NHSWBP: Cohen d=-0.15, 95% CI -0.41 to 0.10). A similar pattern of between-group differences was found for the secondary outcomes. The NHSWBP group generally had larger within-group effects than the other groups and displayed a greater rate of change compared to the other groups on all outcomes, except for gratitude, where the rate of change was greatest for the MPS group. CONCLUSIONS: Our analyses provided encouraging results for the use of brief digital psychological interventions in improving PWB among health and social care workers. Future multisite RCTs, with power to reliably detect differences, are needed to determine the efficacy of contextualized interventions relative to existing digital treatments. TRIAL REGISTRATION: ISRCTN Registry (ISRCTN) ISRCTN18107122; https://www.isrctn.com/ISRCTN18107122.
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 | 0,007 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,001 |
| Méta-épidémiologie (sens large) | 0,005 | 0,004 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,004 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,017 | 0,002 |
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 ».