Systemic Challenges in Internship Training for Health-Service Psychology: A Call to Action From Trainee Stakeholders
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.089 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.027 | 0.037 |
| Scholarly communication | 0.021 | 0.025 |
| Open science | 0.004 | 0.030 |
| Research integrity | 0.027 | 0.049 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".