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Record W3016191981 · doi:10.1177/1524838020915622

Community Change Programs for Children and Youth At-Risk: A Review of Lessons Learned

2020· review· en· W3016191981 on OpenAlexaboutno aff
Daphna Gross‐Manos, Ayala Cohen, Jill E. Korbin

Bibliographic record

VenueTrauma Violence & Abuse · 2020
Typereview
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsnot available
Fundersnot available
KeywordsPublic relationsGovernment (linguistics)Psychological interventionContext (archaeology)Youth workNeglectPolitical sciencePrivate sectorEconomic growthBusinessMedicineNursing

Abstract

fetched live from OpenAlex

The significant role of the community in the lives of children and youth at-risk has become increasingly clear to social work academics and professionals over the last three decades. Alongside the more traditional individual and family responses, community interventions have been designed to catalyze change in the environment of children and youth at-risk and supply holistic and sustainable responses to their needs. Ten such community intervention programs were identified from the United States, Australia, Canada, and Israel. Most employed the community development model, focused on developing leadership and social capital (improving community networking) and advancing coordination between the organizations and sectors in the field of risk among children and youth. The diverse programs reviewed focused both on at-risk children and youth in general or specifically on child abuse and neglect. The programs originated from different health, education, and welfare disciplines and sponsoring authorities. The majority were funded originally by private foundations; however, government involvement was significant, particularly in the adoption and support of initiatives after their development. The current analysis of the programs refers to core issues that arose from the review: professional orientation, main target unit, main initiator, and research and evaluation. Analysis of program characteristics enables identifying relevant aspects of these programs for use by policy, governmental, and nonprofit sector stakeholders seeking to develop similar programs. Conclusions and recommendations to advance the field are suggested considering the current context of government cuts in welfare funds.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.954
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.216
GPT teacher head0.406
Teacher spread0.190 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations13
Published2020
Admission routes1
Has abstractyes

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