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Record W4235707481 · doi:10.1037/cap0000245.supp

Supplemental Material for A Survey of Measures Used to Assess Brain Function at FASD Clinics in Canada

2020· article· en· W4235707481 on OpenAlexaffabout
Kelly Coons-Harding, Katherine Flannigan, Colleen Burns, Audrey McFarlane

Bibliographic record

VenueCanadian Psychology/Psychologie canadienne · 2020
Typearticle
Languageen
FieldMedicine
TopicTreatment of Major Depression
Canadian institutionsLaurentian University
Fundersnot available
KeywordsPsychologyBrain functionClinical psychologyFunction (biology)Applied psychologyPsychiatryNeuroscience

Abstract

fetched live from OpenAlex

What is this project about?The purpose of this project is to collect information about the testing measures used by FASD clinicians across Canada to evaluate neurodevelopmental functioning in their clients.The overall goals of the project are to:-Provide an overview of the measuses used in Canada to assess for FASD across the lifespan -Determine which measures are most commonly used by Canadian FASD clinicians -Find any areas of FASD assessment where adequate assessment measures may be lacking -Identify additional measures that may be considered beyond those recommended in the current FASD diagnostic guideline What will we ask you to do?We are asking FASD assessment and diagnostic clinicians (i.e., physicians, psychologists, speech language therapists, and occupational therapists) to complete an online survey.The survey will take approximately 20 minutes to complete.Participation in this study is voluntary and you have the right not to participate.You can discontinue your participation and withdraw from the study at any time without penalty.You may choose to exit the survey by simply closing the browser page.If you choose to discontinue your participation, we will only use the data collected prior to participant withdrawal as it directly relates to the study purpose and procedures.Are there any risks if I agree to participate?There is an extremely minimal social risk associated with this survey.For example, although no individual-level data will be reported, readers may be able to identify which clinics have participated or which clinics are using particular measures, especially if you work at a small or specialized diagnostic clinic.However, this risk will be minimized by aggregating questionnaire data and no individual clinic information will be available.We believe that this risk is not beyond a risk that you may encounter in your daily practice.How can you benefit if you agree to participate?There are no direct personal benefits to participation in this study.However, information we gather will provide us with a better understanding of the measures used by clinicians to assess for FASD, and help us to increase consistency, best practices, and sharing of useful information that can benefit other clinics.Ultimately, identifying a comprehensive, reliable, and usable testing battery for FASD assessment will improve the clarity and accuracy of the diagnostic process and facilitate advancements in the field.How will your information be used?Once the surveys have been completed, we will aggregate the data collected with all identifiers removed.All information we collect will be private and kept confidential.No one will see the answers you give to questions except members of the research team.All information will be stored in a password-protected file.You personal information, such as your clinic name and clinical role, will be kept separate from your survey responses.

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)0.002
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: Observational · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.209
Threshold uncertainty score0.699

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.008
Science and technology studies0.0040.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.2090.020

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 head0.371
Teacher spread0.187 · 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
GenreMethods

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

Citations0
Published2020
Admission routes2
Has abstractyes

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