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Record W4386831237 · doi:10.1145/3616381

“What’s the Point of Having This Conversation?”: From a Telephone Crisis Helpline in Bangladesh to the Decolonization of Mental Health Services

2023· article· en· W4386831237 on OpenAlexaff
Ananya Bhattacharjee, Sharifa Sultana, Mohammad Ruhul Amin, Yeshim Iqbal, Syed Ishtiaque Ahmed

Bibliographic record

VenueACM Journal on Computing and Sustainable Societies · 2023
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMental healthActive listeningHonourConversationMedicineDisengagement theoryMental health servicePsychologySociologyPublic relationsNursingPsychiatryPsychotherapistPolitical scienceLawGerontology

Abstract

fetched live from OpenAlex

Most of the HCI work on mental health is based on the Western metaphysical definition of mind that is less applicable outside the West. This article focuses on this issue and critically examines “ Kaan Pete Roi ” (KPR), a suicide prevention and emotional support helpline in Bangladesh, through an interview study with 20 participants. We find that KPR’s service, grounded in the “befriending” model—originating from the UK and emphasizing non-judgmental active listening without offering direct advice—often struggles to ensure callers’ safety, provide long-term support, and protect volunteers from harassment and distress. We argue that such failures are often rooted in some foundational ideas of the UK-born “befriending” model that underpins the service. Building on Enrique Dussel’s decolonial philosophy, we argue that “befriending” model and its underpinning Western metaphysical ideation of mind carry a colonial impulse, and we discuss how community-based approaches may better address the mental health problems in the Global South.

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)0.005
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.088

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0270.018
Scholarly communication0.0070.010
Open science0.0020.009
Research integrity0.0040.008
Insufficient payload (model declined to judge)0.0070.002

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 head0.017
GPT teacher head0.344
Teacher spread0.327 · 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 designQualitative
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

Citations11
Published2023
Admission routes1
Has abstractyes

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Same venueACM Journal on Computing and Sustainable SocietiesSame topicDigital Mental Health InterventionsFrench-language works237,207