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Record W4243036648 · doi:10.4073/csr.2006.14

School Feeding for Improving the Physical and Psychosocial Health of Disadvantaged Students

2006· article· en· W4243036648 on OpenAlexaff
Betsy Kristjansson, Vivian Robinson, Mark Petticrew, Barbara Macdonald, Julia Krasevec, Laura Janzen, Trisha Greenhalgh, George A. Wells, Jessie MacGowan, Anna Farmer, Beverley Shea, Alain Mayhew, Peter Tugwell

Bibliographic record

VenueCampbell Systematic Reviews · 2006
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsInstitute of Population and Public HealthHospital for Sick ChildrenUniversity of Ottawa
Fundersnot available
KeywordsDisadvantagedPsychosocialCINAHLPsycINFOAttendanceMedicineMental healthRandomized controlled trialMEDLINEGerontologyPsychologyNursingPsychiatryPsychological intervention

Abstract

fetched live from OpenAlex

The main objective of this systematic review was to determine the effectiveness of school feeding programs in improving physical and psychosocial health for disadvantaged school pupils. A comprehensive search was conducted up to May 2006. We included 18 studies. School meals may have some small benefits for disadvantaged children. We recommend further well-designed studies on the effectiveness of school meals be undertaken, that results should be reported according to socio-economic status, and that researchers gather robust data on both processes and carefully chosen outcomes. Abstract Background Early malnutrition and/or micronutrient deficiencies can adversely affect physical, mental, and social aspects of child health. School feeding programs are designed to improve attendance, achievement, growth, and other health outcomes. Objectives The main objective was to determine the effectiveness of school feeding programs in improving physical and psychosocial health for disadvantaged school pupils. Search strategy We searched a number of databases including CENTRAL (2006 Issue 2), MEDLINE (1966 to May 2006), EMBASE (1980 to May 2006), PsycINFO (1980 to May 2006) and CINAHL (1982 to May 2006). Grey literature sources were also searched. Reference lists of included studies and key journals were handsearched and we also contacted selected experts in the field. Selection criteria Data from randomized controlled trials (RCTs), non-randomised controlled clinical trials (CCTs), controlled before and after studies (CBAs), and interrupted time series studies (ITSs) were included. Feeding had to be done in school; the majority of participants had to be socio-economically disadvantaged. Data collection & analysis Two reviewers assessed all searches and retrieved studies. Data extraction was done by one of four reviewers and reviewed by a second. Two reviewers independently rated quality. If sufficient data were available, they were synthesized using random effects meta-analysis, adjusting for clustering if needed. Analyses were performed separately for RCTs and CBAs and for higher and lower income countries. Main results We included 18 studies. For weight, in the RCTs and CBAs from Lower Income Countries, experimental group children gained an average of 0.39 kg (95% C.I: 0.11 to 0.67) over an average of 19 months and 0.71 kg (95% C.I.: 0.48 to 0.95) over 11.3 months respectively. Results for weight were mixed in higher income countries. For height, results were mixed; height gain was greater for younger children. Attendance in lower income countries was higher in experimental groups than in controls; our results show an average increase of 4 to 6 days a year. Math gains were consistently higher for experimental groups in lower income countries; in CBAs, the Standardized Mean Difference was 0.66 (95% C.I. = 0.13 to 1.18). In short-term studies, small improvements in some cognitive tasks were found. Reviewers’ conclusions School meals may have some small benefits for disadvantaged children. We recommend further well-designed studies on the effectiveness of school meals be undertaken, that results should be reported according to socio-economic status, and that researchers gather robust data on both processes and carefully chosen outcomes.

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)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.009
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.021
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.005
Bibliometrics0.0060.005
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.032
GPT teacher head0.365
Teacher spread0.332 · 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 designSystematic review
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

Citations50
Published2006
Admission routes1
Has abstractyes

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