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Record W3190853121 · doi:10.1037/spq0000450

The practices of psychologists working in schools during COVID-19: A multi-country investigation.

2021· article· en· W3190853121 on OpenAlexaboutno aff
Andrea Reupert, Gary E. Schaffer, Alexa von Hagen, Kelly‐Ann Allen, Emily Berger, Gerhard Büttner, Elizabeth Power, Zoë Morris, Pascale Paradis, Amy K. Fisk, Dianne Summers, Gerald Wurf, Fiona May

Bibliographic record

VenueSchool Psychology · 2021
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsnot available
Fundersnot available
KeywordsPsycINFOMental healthSnowball samplingTelehealthPsychologyMedical educationCoronavirus disease 2019 (COVID-19)PandemicMEDLINEMedicinePolitical scienceTelemedicineHealth carePsychiatry

Abstract

fetched live from OpenAlex

= 85) completed the online survey. Overall, school psychology services across these four countries pivoted from psychoeducational assessments to virtual counseling, consultation, and the development/posting of online support directly to children or parents to use with their children. There was some variation between countries; during the pandemic, significantly more psychologists in Germany and Australia provided telehealth/telecounseling than those in the United States and Canada, and psychologists in Germany provided significantly more hardcopy material to support children than psychologists in other countries. There is a need to ensure psychologists have the appropriate technological skills to support school communities during periods of school closure, including, but not limited to, virtual counseling and the administration of psychoeducational assessments. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

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)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.051
Threshold uncertainty score0.102

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0030.001
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.184
GPT teacher head0.505
Teacher spread0.321 · 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 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

Citations36
Published2021
Admission routes1
Has abstractyes

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