MétaCan
Menu
Back to cohort
Record W2767686226 · doi:10.1016/j.ssmph.2017.11.002

Becoming a ‘pharmaceutical person’: Medication use trajectories from age 26 to 38 in a representative birth cohort from Dunedin, New Zealand

2017· article· en· W2767686226 on OpenAlexafffund
Peri J. Ballantyne, Pauline Norris, V. P. B. Parachuru, W. Murray Thomson

Bibliographic record

VenueSSM - Population Health · 2017
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical studies and practices
Canadian institutionsUniversity of TorontoTrent University
FundersMinistry of Health, British Columbia
KeywordsMedicalizationCohortPsychological interventionMedicineCohort studyYoung adultDemographyGerontologyPsychiatry

Abstract

fetched live from OpenAlex

Despite the abundance of medications available for human consumption, and frequent concerns about increasing medicalization or pharmaceuticalization of everyday life, there is little research investigating medicines-use in young and middle-aged populations and discussing the implications of young people using increasing numbers of medicines and becoming pharmaceutical users over time. We use data from a New Zealand longitudinal study to examine changes in self-reported medication use by a complete birth cohort of young adults. Details of medications taken during the previous two weeks at age 38 are compared to similar data collected at ages 32 and 26, and by gender. Major drug categories are examined. General use profiles and medicine-types are considered in light of our interest in understanding the formation of the young and middle-aging 'pharmaceutical person' - where one's embodied experience is frequently and normally mediated by pharmaceutical interventions having documented benefit/risk outcomes.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.137
Threshold uncertainty score0.860

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.233
GPT teacher head0.488
Teacher spread0.255 · 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

Citations3
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueSSM - Population HealthSame topicPharmaceutical studies and practicesFrench-language works237,207