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Record W4282922818 · doi:10.1093/jhmas/jrac013

Cyber Solace: Historicizing an Online Forum for Patients with Depression, 1990-1999

2022· article· en· W4282922818 on OpenAlexaff
Daniel Q. Huang

Bibliographic record

VenueJournal of the History of Medicine and Allied Sciences · 2022
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsQueen's University
Fundersnot available
KeywordsAudience measurementMental healthEmpowermentDepression (economics)CyberculturePsychologySociologyMedia studiesInternet privacyPsychiatryPolitical scienceThe InternetComputer scienceLawWorld Wide Web

Abstract

fetched live from OpenAlex

Alt.support.depression (ASD) was an online forum for patients with depression that operated in the 1990s on the computer network Usenet. At its peak, the forum had an estimated readership of 57,000 and saw upwards of 500 posts a day. Aligning with recent efforts by historians to deinstitutionalize the history of psychiatry, this study traces the emergence of ASD as a new extramural space for mental health care in the 1990s. Its users created a unique therapeutic milieu informed by the consumer-survivor movement and 1990s cyberculture. As ASD grew in size and complexity, its users sought to redesign their forum, opening what had previously been a technological black box. Working by a process of inscription-a concept described by science and technology studies scholar Madeleine Akrich-they created a unique psychiatric constituency whose attitude towards technologies of mental health care was neither submissive nor subversive. Rather, the forum's users developed their own notions of patient empowerment and lay expertise in psychiatry.

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)0.001
metaresearch head score (Gemma)0.006
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 score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.003
Scholarly communication0.0030.004
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.225
GPT teacher head0.391
Teacher spread0.166 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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