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Theory of Change in Sports-Based Urban Youth Programs: Lessons from Creating Chances

2021· reference-entry· en· W3202603447 on OpenAlexaboutno aff
Rachel Baffsky, Lynn Kemp, Anne Bunde‐Birouste

Bibliographic record

VenueOxford Research Encyclopedia of Global Public Health · 2021
Typereference-entry
Languageen
FieldSocial Sciences
TopicYouth Development and Social Support
Canadian institutionsnot available
Fundersnot available
KeywordsTheory of changeBlueprintPublic relationsPsychologySocial changePositive Youth DevelopmentPolitical scienceEngineeringManagementDevelopmental psychologyEconomics

Abstract

fetched live from OpenAlex

Abstract Sports-based positive youth development (SB-PYD) programs are health promotion programs that intentionally use sports to build life skills and leadership capacity among young people at risk of social exclusion. The defining characteristics of SB-PYD programs are that they are strengths-based, holistic, and use sports as a vehicle to maximize young people’s health, social, and educational outcomes. SB-PYD programs aim to enhance modifiable social determinants of health (such as social inclusion) by explicitly addressing three Ottawa charter action areas; strengthening community action, developing personal skills, and creating supportive environments. These programs have been increasingly implemented since the early 2000s to address the United Nations’ sustainable development goals. Despite their growth, research indicates that SB-PYD programs are often designed, implemented, and evaluated without evidence-based theories of change. An evidence-based theory of change is a visual depiction of a program’s assumptions, activities, contextual factors, and outcomes supported by scientific findings. A lack of evidence-based theory of change becomes problematic at the implementation phase when practitioners are trying to determine if their programs should be adapted or fixed. Without an evidence-based theory of change, practitioners are making changes based on their intuition, which limits program outcomes. However, the process of developing a theory of change is time-consuming and resource intensive. Multiple calls to action have been made for SB-PYD practitioners who have successfully developed evidence-based theories of change to share their process with others in the field. This will provide a blueprint for other SB-PYD practitioners to develop and articulate their own theories of change to optimize program development and adaptation. Traditional translational research models assume the development of an evidence-based theory of change is the first step in a linear process of developing a sustainable health promotion program. However, in the 2010s, researchers started to observe that the development and adaptation of health promotion programs was rarely a linear process in reality, and that case studies are needed to provide empirical support for this claim. It is valuable for SB-PYD practitioners to consider the benefits of using translational research to develop and revise evidence-based theories of change for programs at any stage of implementation to maximize their public health impact.

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)0.014
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.002
Science and technology studies0.0050.019
Scholarly communication0.0070.009
Open science0.0040.007
Research integrity0.0030.005
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 head0.257
GPT teacher head0.446
Teacher spread0.189 · 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 designTheoretical or conceptual
Domainnot available
GenreOther

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".

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Citations1
Published2021
Admission routes1
Has abstractyes

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