North American Perspectives on Education, Training, Licensing, and Credentialing
Bibliographic record
Abstract
Abstract This chapter addresses the broad realm of education, training, licensing and credentialing in professional psychology in the largest countries on the North American continent. Particular attention is paid to the development and implementation of similar mechanisms in both the United States and Canada due to similar developmental histories and strong cross‐border interchanges and influences. Information obtained on Mexico from the series of Tri‐Lateral Forum meetings on Professional Psychology is also included. After addressing the definition of clinical (small c) psychology, professional training (practica, internship, and postdoctoral components) and certification mechanisms in professional psychology (licensure and credentialing) are used to help define competence in professional psychology. The role of each credential is described in the United States and Canada and to some degree in Mexico. Then the structures established to educate, train, accredit, identify, and certify programs and individuals are presented. Influences from external forces that have shaped, and continue to shape these developments, are included and an historical addendum on the national conferences in the United States and Canada that have shaped health service psychology are provided. This chapter is oriented toward an individual student or professional looking for a single source on education, training, licensing, and credentialing in professional psychology.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".