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Record W3137147922 · doi:10.1177/08295735211000513

Implementing Evidence-Based Practices in School Psychology: Excavation by De-Implementing the Disproved

2021· article· en· W3137147922 on OpenAlexaffabout
Steven R. Shaw

Bibliographic record

VenueCanadian Journal of School Psychology · 2021
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsMcGill University
Fundersnot available
KeywordsBest practiceValue (mathematics)School psychologyPsychologyEvidence-based practiceBest evidencePsychological interventionPedagogyEngineering ethicsMedical educationMedicinePsychiatryAlternative medicineManagementEngineering

Abstract

fetched live from OpenAlex

The scientist-practitioner model of practice is the most common approach to the profession of school psychology and embraces evidence-based practices as foundations of clinical practice. The focus on evidence-based practices involves not only using the preponderance of research to determine what works, but also how to implement these practices effectively. An important impediment to implementing innovative evidence-based practices is that interventions and practices that have been proved ineffective or of low value continue to be used in education and psychology. What are the issues that assist in discontinuing practices that are widely used, but have been disproved or are otherwise problematic? How can room be made for more effective, innovative, and evidence-based practices? This issue of the Canadian Journal of School Psychology is devoted to exploration of different forms of disproved, low value, or problematic practices, factors that keep these practices alive in schools, and how to best de-implement ineffective, low value, and problematic practices. If the scientist-practitioner model is to be defined largely by the implementation of evidence-based practices, then de-implementation will be a critical aspect in the evolution of the profession of school psychology.

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)0.018
metaresearch head score (Gemma)0.031
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.256
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0180.031
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0070.000

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 head0.598
GPT teacher head0.679
Teacher spread0.081 · 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 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

Citations21
Published2021
Admission routes2
Has abstractyes

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