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Record W2792423919 · doi:10.1177/1359105318755156

Notes on the development of health psychology and behavioral medicine in the United States

2018· article· en· W2792423919 on OpenAlexaff
Ian Lubek, Monica A. Ghabrial, Naomi Ennis, Sara Crann, Amanda J. Jenkins, Michelle Green, Joel John Badali, William Salmon, Janice Moodley, Elizabeth Sulima, Jeffery Yen, Kieran C. O’Doherty, Paula C. Barata

Bibliographic record

VenueJournal of Health Psychology · 2018
Typearticle
Languageen
FieldPsychology
TopicAcademic and Historical Perspectives in Psychology
Canadian institutionsMcGill UniversityToronto Metropolitan UniversityUniversity of TorontoUniversity of Guelph
Fundersnot available
KeywordsHealth psychologyContext (archaeology)Behavioral medicineHistoriographyDisciplineCritical psychologyHistoricismBehavioural sciencesHistory of psychologyState (computer science)PsychologySociologySocial scienceMedicinePublic healthPolitical sciencePsychiatryPsychoanalysisHistoryLaw

Abstract

fetched live from OpenAlex

A "standard" historiographical overview of the development of health psychology in the United States, alongside behavioral medicine, first summarizes previous disciplinary and professional histories. A "historicist" approach follows, focussing on a collective biographical summary of accumulated contributions of one cohort (1967-1971) at State University of New York at Stony Brook. Foundational developments of the two areas are highlighted, contextualized within their socio-political context, as are innovative cross-boundary collaboration on "precursor" studies from the 1960s and 1970s, before the official disciplines emerged. Research pathways are traced from social psychology to health psychology and from clinical psychology to behavioral medicine.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.994
Threshold uncertainty score0.090

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 studies0.0060.006
Scholarly communication0.0030.002
Open science0.0000.003
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0040.000

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 head0.193
GPT teacher head0.534
Teacher spread0.341 · 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.

Study designNot applicable
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

Citations6
Published2018
Admission routes1
Has abstractyes

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