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Record W4294739186 · doi:10.3390/ijerph191711015

Stress across the Lifespan: From Risk to Management—Conference Report on the Inaugural Canadian Stress Research Summit

2022· article· en· W4294739186 on OpenAlexafffundabout
Alexandra Fiocco

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2022
Typearticle
Languageen
FieldHealth Professions
TopicHealth, psychology, and well-being
Canadian institutionsToronto Metropolitan University
FundersCanadian Institutes of Health Research
KeywordsSummitLibrary scienceStress (linguistics)Mental healthPolitical sciencePsychologyGerontologySociologyMedicineGeographyComputer sciencePsychiatry

Abstract

fetched live from OpenAlex

In 2021, the Toronto Metropolitan University Institute for Stress and Wellbeing Research welcomed over 200 conference delegates across Canada to the inaugural Canadian Stress Research Summit (CSRS) to share ideas and foster collaboration among Canadian scholars. This conference was unique from existing international stress-related conferences as it bridged science and community. The objective of this conference report is to provide an overview of the 3-day virtual inaugural stress conference, offering a summary of the keynote addresses, themed symposia, spotlight presentations, graphical designs of selected presentations, and conference feedback. Overall, the CSRS highlighted important methodological considerations in understanding the relationship between stress exposure and various outcomes of interest that pertain to the mental health and wellbeing of Canadians. Furthermore, there is a need for continued work to understand stress across the lifespan from an inclusive and diverse Canadian lens.

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)0.010
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.931
Threshold uncertainty score0.499

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0140.002
Scholarly communication0.0080.002
Open science0.0020.007
Research integrity0.0020.007
Insufficient payload (model declined to judge)0.0090.002

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 head0.186
GPT teacher head0.516
Teacher spread0.330 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations1
Published2022
Admission routes3
Has abstractyes

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