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Record W3025744743 · doi:10.1080/10474412.2020.1759428

Cost-effectiveness of Consultation for a Daily Report Card Intervention: Comparing In-Person and Online Implementation Strategies

2020· article· en· W3025744743 on OpenAlexfundno aff
Julie Sarno Owens, Samantha M. Margherio, Mary Lee, Steven W. Evans, D. Max Crowley, Erika K. Coles, Clifton S. Mixon

Bibliographic record

VenueJournal of Educational and Psychological Consultation · 2020
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsnot available
FundersInstitute of Education SciencesPolicyWise for Children and Families
KeywordsIntervention (counseling)Report cardPsychologyMedical educationComputer scienceApplied psychologyMedicinePedagogyPsychiatry

Abstract

fetched live from OpenAlex

Teachers can implement a high quality Daily Report Card (DRC) intervention when they receive face-to-face consultation or interactive online supports. Yet, it is unclear which method is most cost-effective. Using an ingredients-based approach and societal perspective, we examined costs and cost-effectiveness (compared to typical practice) of three implementation strategies (face-to-face standard consultation, face-to-face enhanced consultation, interactive online supports) with 112 elementary school teachers. Teachers received consultation for DRC implementation with one student with or at risk for ADHD. Over 2 months, we collected data on teachers’ implementation and changes in student behaviors. Regarding cost per student, enhanced consultation was the most costly ($864), followed by standard consultation ($634) and interactive online supports ($307). Regarding cost-effectiveness (costs required to achieve the desired effect beyond typical practice), interactive online supports were the most cost-effective followed by enhanced consultation and standard consultation. We discuss implications for research and maximizing outcomes given dollars spent.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score0.308

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.342
GPT teacher head0.526
Teacher spread0.184 · 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 designObservational
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

Citations7
Published2020
Admission routes1
Has abstractyes

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