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Record W4220960731 · doi:10.1177/21677026211072232

Systemic Challenges in Internship Training for Health-Service Psychology: A Call to Action From Trainee Stakeholders

2022· article· en· W4220960731 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueClinical Psychological Science · 2022
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsUniversity of Calgary
FundersNational Institute on Drug AbuseNational Institute on Alcohol Abuse and Alcoholism
KeywordsCall to actionPsychologyInternshipTraining (meteorology)PandemicAction (physics)Medical educationPerspective (graphical)Coronavirus disease 2019 (COVID-19)Public relationsApplied psychologyPolitical scienceMedicineBusiness

Abstract

fetched live from OpenAlex

The challenges observed in health service psychology (HSP) training during COVID-19 revealed systemic and philosophical issues that preexisted the pandemic, but became more visible during the global health crisis. In a position paper written by 23 trainees across different sites and training specializations, the authors use lessons learned from COVID-19 as a touchstone for a call to action in HSP training. Historically, trainee voices have been conspicuously absent from literature about clinical training. We describe longstanding dilemmas in HSP training that were exacerbated by the pandemic and will continue to require resolution after the pandemic has subsided. The authors make recommendations for systems-level changes that would advance equity and sustainability in HSP training. This article advances the conversation about HSP training by including the perspective of trainees as essential stakeholders.

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.

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)0.015
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.799
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.002
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 head0.853
GPT teacher head0.673
Teacher spread0.179 · 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