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Record W4405827783 · doi:10.53841/bpsfpid.2024.22.3.12

How can we promote working in intellectual disability services? Summary of a workforce engagement event

2024· article· en· W4405827783 on OpenAlexaff
Alison Spencer, J Crabtree, Cathy Randle‐Phillips, Jenni Shaw, Kate Theodore, Hayley Thomas

Bibliographic record

VenueFPID Bulletin The Bulletin of the Faculty for People with Intellectual Disabilities · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicHealthcare innovation and challenges
Canadian institutionsSt. Thomas Hospital
Fundersnot available
KeywordsWorkforceWork (physics)Event (particle physics)Join (topology)Plan (archaeology)Intellectual disabilityPublic relationsPsychologyBusinessMedical educationPolitical scienceEngineeringMedicine

Abstract

fetched live from OpenAlex

Summary An online event was held for psychologists who work in intellectual disability services and in universities. We wanted to share the work we are doing to encourage new psychologists to join ID services. Lots of ideas were discussed and we plan to take some of these forward as projects.

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)0.009
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0130.001
Scholarly communication0.0080.004
Open science0.0010.009
Research integrity0.0100.017
Insufficient payload (model declined to judge)0.0200.004

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 head0.064
GPT teacher head0.321
Teacher spread0.256 · 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 designQualitative
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

Citations1
Published2024
Admission routes1
Has abstractyes

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