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Record W4285093046 · doi:10.1080/10437797.2022.2089305

Support and Aid to Families Electronically (SAFE): Addressing Intersecting Academic and Community Needs

2022· article· en· W4285093046 on OpenAlexfundno aff
Jane E. Sanders, Hazel Antia, Esther Bernal, Jessica Landon, Andrew James Reed, Ariel Seale, Hayley Sullivan, Emma Sutton, M.K. Arundel, Rick Csiernik

Bibliographic record

VenueJournal of Social Work Education · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Work Education and Practice
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPracticumSocial workGeneral partnershipMedical educationMental healthWork (physics)PsychologyPedagogyNursingSociologyMedicinePolitical scienceEngineering

Abstract

fetched live from OpenAlex

In response to the COVID-19 pandemic, schools from elementary to post-secondary suddenly shifted to remote learning, placing a heavy burden on parents and caregivers. Likewise, social work practicum placements pivoted to remote learning, compounding existing difficulties securing practicum opportunities. This article describes a response to these challenges. The Support and Aid to Families Electronically (SAFE) practicum pilot program was developed through a community-university partnership between the King’s University College at Western University’s School of Social Work and the Thames Valley District School Board. SAFE addresses parental stress and mental health through free and immediate online counselling, while providing stable remote practicum placements. SAFE provides a model for increasing practicum opportunities while simultaneously supporting the needs of underserviced communities.

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)0.003
metaresearch head score (Gemma)0.006
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0050.001
Scholarly communication0.0020.002
Open science0.0010.007
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.001

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 head0.051
GPT teacher head0.398
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 designNot applicable
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

Citations8
Published2022
Admission routes1
Has abstractyes

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