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Record W7075352375

"Shared and Collective Stress": 2SLGBTQI and Allied Mental Healthcare Providers' Experiences and Challenges During COVID-19 in Canada

2024· article· en· W7075352375 on OpenAlexaboutno aff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicTheoretical and Computational Physics
Canadian institutionsnot available
Fundersnot available
KeywordsService providerMental healthContext (archaeology)Service (business)Focus groupService delivery frameworkHealth carePandemic
DOInot available

Abstract

fetched live from OpenAlex

The confluence of increased demand for mental health services and decreased resources due to the COVID-19 pandemic has created multiple challenges for mental healthcare and social service providers. 2SLGBTQI service providers may be disproportionately impacted by pandemic-related challenges, such as psychological distress, vicarious traumatization, and burnout. However, there are significant knowledge gaps regarding the needs and experiences of 2SLGBTQI and allied service providers in the context of the COVID-19 pandemic in Canada. To address these gaps, we conducted a national survey (N = 304), eight semi-structured focus groups, and five semi-structured interviews (N = 61) with 2SLGBTQI care seekers and service providers across Canada. Based on data from the 106 2SLGBTQI service providers and 3 allied service providers who took part in these research activities, this paper explores the challenges service providers encounter when providing care to 2SLGBTQI individuals as well as their adaptive responses to these challenges. Understanding the experiences of service providers who share lived experiences of discrimination and marginalization with their clients is critical to addressing barriers to affirming mental healthcare, shifting services to meet the evolving needs of both care seekers and providers, and developing upstream, comprehensive solutions to address the causes of 2SLGBTQI mental health disparities.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.249
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.138
GPT teacher head0.474
Teacher spread0.336 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations0
Published2024
Admission routes1
Has abstractyes

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