Inclusion of Trainee Stakeholders Is Necessary for Effective Change in Health-Service-Psychology Internship Training
Bibliographic record
Abstract
In a recent call to action, we described pressing issues in the health-service-psychology (HSP) internship from the perspective of interns. In our article, we sought to initiate a dialogue that would include trainees and bring about concrete changes. The commentaries on our article are a testament to the readiness of the field to engage in such a dialogue, and we applaud the actionable recommendations that they make. In our response to these commentaries, we seek to move the conversation further forward. We observe two themes that cut across these responses: the impetus to gather novel data on training (the "need to know") and the importance of taking action (the "need to act"). We emphasize that in new efforts to gather data and take policy-level action, the inclusion of trainee stakeholders (as well as others involved in and affected by HSP training) is a crucial ingredient for sustainable and equitable change.
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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 | 0.134 | 0.158 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.026 | 0.031 |
| Scholarly communication | 0.018 | 0.021 |
| Open science | 0.004 | 0.024 |
| Research integrity | 0.017 | 0.025 |
| Insufficient payload (model declined to judge) | 0.004 | 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".