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Record W4391774166 · doi:10.32920/25213019.v1

Training in Child Welfare: A Worker’s Perspective

2024· preprint· en· W4391774166 on OpenAlexaffabout
Mary Araman

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicSocial Work Education and Practice
Canadian institutionsToronto Metropolitan UniversityBrock University
Fundersnot available
KeywordsWelfareTraining (meteorology)Perspective (graphical)Sample (material)PsychologyFront lineApplied psychologyWork (physics)Medical educationSocial psychologyMedicinePolitical scienceEngineeringComputer science

Abstract

fetched live from OpenAlex

This Major Research Paper will explore child protection workers’ unique experiences with the training they receive in preparation for the work they do. This study used a sample of five workers from different agencies who were provided an open-ended questionnaire. The research question was whether the workers’ feel confident in the training they received and whether they feel this training meets their needs as front-line workers. This study uses a Systems Theory Approach, viewing worker training as a piece of the child welfare system that is integral to its overall functioning and outcomes. The findings indicated that workers in Ontario felt there were areas of training that could be improved. The participants indicated that they had been involved in situations for which they felt inadequately trained. The implications for child welfare practice are numerous. Accessing firsthand knowledge on how the workers perceive their current level of training can be a starting point to implement a more wholistic training program. This could in turn influence the outcomes of practice.

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)0.006
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.067
Threshold uncertainty score0.134

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0170.013
Scholarly communication0.0080.004
Open science0.0010.004
Research integrity0.0030.005
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 head0.067
GPT teacher head0.414
Teacher spread0.347 · 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

Citations0
Published2024
Admission routes2
Has abstractyes

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