MétaCan
Menu
Back to cohort
Record W2160912945

Psychopharmacology Training and Canadian Counsellors: Are We Getting What We Want and Need?

2008· article· en· W2160912945 on OpenAlexaboutno aff
David Schaefer, Gina Wong‐Wylie

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicTreatment of Major Depression
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyPsychopharmacologyMedical educationTraining (meteorology)Graduate studentsClinical psychologyPsychotherapistMedicinePsychiatry
DOInot available

Abstract

fetched live from OpenAlex

The psychopharmacology training experiences and attitudes of Canadian counsellors were the focus of our national Internet-based survey. This study was part of a larger investiga-tion on Canadian counsellors ’ attitudes, practices, and training experiences related to clients on antidepressants. Results of the current study indicate Canadian counsellors vary considerably in type and amount of psychopharmacology training received. A majority of participants reported that they did not receive this type of training in graduate school, though a strong majority advocated for such training to be mandatory in graduate school. Limitations of this research and directions for future research are provided. résumé Les expériences de formation en psychopharmacologie et les attitudes des conseillers canadiens étaient le point focal de notre sondage national par Internet. Cette étude faisait partie d’une plus grande enquête sur les attitudes des conseillers canadiens, leurs pratiques et leurs expériences de formation en matière de clients sous antidépresseurs. Les résultats de la présente étude indiquent que le type et la quantité de formations

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)0.009
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.983
Threshold uncertainty score0.159

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0050.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.049
GPT teacher head0.300
Teacher spread0.251 · 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

Citations1
Published2008
Admission routes1
Has abstractyes

Explore more

Same topicTreatment of Major DepressionFrench-language works237,207