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Record W4404956455 · doi:10.1080/01488376.2024.2429668

Helping Others While Helping Yourself: Vicarious Resilience in Victim Service Workers With and Without Experiences of Victimization

2024· article· en· W4404956455 on OpenAlexafffundabout
Diana McGlinchey

Bibliographic record

VenueJournal of Social Service Research · 2024
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsAlgonquin College
FundersSocial Sciences and Humanities Research Council
KeywordsPsychological resiliencePsychologySocial psychologyResilience (materials science)Posttraumatic growthService (business)Affect (linguistics)Service providerField (mathematics)BusinessCommunication

Abstract

fetched live from OpenAlex

Vicarious resilience refers to the positive effects that a service provider experiences through witnessing the healing, recovery, and resilience of the people they serve. This study used a sample of 804 victim service providers (VSPs) across Canada who completed the Vicarious Resilience Scale (VRS) and answered a closed-ended survey question about why they chose to work or volunteer in the field. Results show that VSPs who entered the field because they have previous experience of victimization had significantly higher VRS scores than their counterparts who entered the field for any other reason (p < .001). It appears that growth in vicarious resilience in VSPs may be spurred by their own experiences of victimization in a process parallel to that modeled on the concepts of “wounded healers” and posttraumatic growth. This furthers our understanding of factors that affect vicarious resilience. Implications for training and supervision of VSPs and recommendations for future studies, are discussed.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.031
Threshold uncertainty score0.451

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.060
GPT teacher head0.443
Teacher spread0.383 · 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 teacher head, 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

Citations2
Published2024
Admission routes3
Has abstractyes

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