MétaCan
Menu
Back to cohort
Record W3172682727 · doi:10.2196/31211

Implementation of an Internet-Based Acceptance and Commitment Therapy for Promoting Mental Health Among Migrant Live-in Caregivers in Canada: Protocol

2021· article· en· W3172682727 on OpenAlexaffvenueabout
Kenneth Fung, Mandana Vahabi, Masoomeh Moosapoor, Abdolreza Akbarian, Jenny J. W. Liu, Josephine Pui‐Hing Wong

Bibliographic record

VenueJMIR Research Protocols · 2021
Typearticle
Languageen
FieldPsychology
TopicFamily Caregiving in Mental Illness
Canadian institutionsPublic Health OntarioInstitute for Clinical Evaluative SciencesToronto Western HospitalUniversity of TorontoToronto Metropolitan UniversityUniversity Health Network
Fundersnot available
KeywordsAcceptance and commitment therapyMental healthPsychologyPsychosocialMindfulnessPsychological resiliencePsychological interventionSocial supportDistressAnxietyClinical psychologyIntervention (counseling)PsychiatryPsychotherapist

Abstract

fetched live from OpenAlex

BACKGROUND: Psychological distress, isolation, feelings of powerlessness, and limited social support are realities faced by temporary migrant live-in caregivers in Canada. Furthermore, they experience multiple barriers in accessing mental health services due to their long work hours, limited knowledge of health resources, precarious employment, and immigration status. OBJECTIVE: The Women Empowerment - Caregiver Acceptance & Resilience E-Learning (WE2CARE) project is a pilot intervention research project that aims to promote the mental well-being and resiliency of migrant live-in caregivers. The objectives include exploring the effectiveness of this program in achieving the following: (1) reducing psychological distress (depression, anxiety, and stress); (2) promoting committed actions of self-care; and (3) building mutual support social networks. Further, participants' satisfaction with the intervention and their perceived barriers to and facilitators of practicing the self-care strategies embedded in WE2CARE will be examined. METHODS: A total of 36 live-in caregivers residing in the Greater Toronto Area will be recruited and randomly assigned to either the intervention or waitlist control group. The intervention group will receive a 6-week web-based psychosocial intervention that will be based on Acceptance and Commitment Therapy (ACT). Standardized self-reported surveys will be administered online preintervention, postintervention, and at 6 weeks postintervention to assess mental distress (Depression, Anxiety and Stress Scale), psychological flexibility (Acceptance and Action Questionnaire), mindfulness (Cognitive and Affective Mindfulness Scale - Revised), and resilience (Multi-System Model of Resilience Inventory). In addition, two focus groups will be held with a subset of participants to explore their feedback on the utility of the WE2CARE program. RESULTS: WE2CARE was funded in January 2019 for a year. The protocol was approved by the research ethics boards of Ryerson University (REB 2019-036) and the University of Toronto (RIS37623) in February and May 2019, respectively. Data collection started upon ethics approval and was completed by May 2020. A total of 29 caregivers completed the study and 20 participated in the focus groups. Data analyses are in progress and results will be published in 2021. CONCLUSIONS: WE2CARE could be a promising approach to reducing stress, promoting resilience, and providing a virtual space for peer emotional support and collaborative learning among socially isolated and marginalized women. The results of this pilot study will inform the adaptation of an ACT-based psychological intervention for online delivery and determine its utility in promoting mental health among disadvantaged and vulnerable populations. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/31211.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.620
Threshold uncertainty score0.755

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.008
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0020.002
Science and technology studies0.0100.002
Scholarly communication0.0020.001
Open science0.0030.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0370.003

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.132
GPT teacher head0.532
Teacher spread0.400 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreProtocol

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2021
Admission routes3
Has abstractyes

Explore more

Same venueJMIR Research ProtocolsSame topicFamily Caregiving in Mental IllnessFrench-language works237,207