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Record W7017243840

Anger in healing from childhood sexual abuse, a group intervention

2000· other· en· W7017243840 on OpenAlexvenueaboutno aff

Bibliographic record

VenueLibrary and Archives Canada (Government of Canada) · 2000
Typeother
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMachine Learning in Bioinformatics
Canadian institutionsnot available
Fundersnot available
KeywordsAngerIntervention (counseling)Sexual abusePracticumAnger managementTreatment and control groupsGroup psychotherapy
DOInot available

Abstract

fetched live from OpenAlex

This practicum consisted of the creation, implementation, and evaluation of a twelve week group program for women, who were impacted by anger in their healing from childhood sexual abuse. Two groups with a total of eleven participants took part in the practicum offered at the Laurel Centre in Winnipeg, Manitoba. The purpose of the Anger in Healing groups were: (1) To assist women who have survived childhood sexual abuse to acknowledge and to honor their experiences and expressions of anger in their healing from the abuse. (2) To examine these experiences using a cost/benefit perspective, and provide women the opportunity to choose whether to continue engaging in the same forms of expression of anger or to choose to try on different forms of expression in experiencing their anger. (3) To explore different ways of expressing and experiencing anger that fit with each individual woman's goals in relation to her healing from the child sexual abuse. (Abstract shortened by UMI.)

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)0.004
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: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0050.001
Scholarly communication0.0010.001
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.002
GPT teacher head0.150
Teacher spread0.149 · 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 designNon-randomized trial
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

Citations0
Published2000
Admission routes2
Has abstractyes

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Same venueLibrary and Archives Canada (Government of Canada)Same topicMachine Learning in BioinformaticsFrench-language works237,207