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Record W2951531792 · doi:10.1136/medhum-2018-011593

Psychedelic crossings: American mental health and LSD in the 1970s

2019· article· en· W2951531792 on OpenAlexaff
Lucas Richert, Erika Dyck

Bibliographic record

VenueMedical Humanities · 2019
Typearticle
Languageen
FieldPsychology
TopicPsychedelics and Drug Studies
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMental healthLysergic acid diethylamideNarrativeThe artsSociologyPoint (geometry)PsychologyMedia studiesCriminologyVisual artsPsychiatryArtMedicineLiterature

Abstract

fetched live from OpenAlex

This article places a spotlight on lysergic acid diethylamide (LSD) and American mental health in the 1970s, an era in which psychedelic science was far from settled and researchers continued to push the limits of regulation, resist change and attempt to revolutionise the mental health market-place. The following pages reveal some of the connections between mental health, LSD and the wider setting, avoiding both ascension and declension narratives. We offer a renewed approach to a substance, LSD, which bridged the gap between biomedical understandings of 'health' and 'cure' and the subjective needs of the individual. Garnering much attention, much like today, LSD created a cross-over point that brought together the humanities and arts, social sciences, health policy, medical education, patient experience and the public at large. It also divided opinion. This study draws on archival materials, medical literature and popular culture to understand the dynamics of psychedelic crossings as a means of engendering a fresh approach to cultural and countercultural-based healthcare during the 1970s.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.042
Threshold uncertainty score0.084

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0140.024
Scholarly communication0.0050.006
Open science0.0000.004
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0030.000

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 head0.040
GPT teacher head0.380
Teacher spread0.339 · 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

Citations17
Published2019
Admission routes1
Has abstractyes

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