A Competency Framework for the Practice of Psychology: Procedures and Implications
Bibliographic record
Abstract
OBJECTIVE: Several competency models for training and practice in professional psychology have been proposed in the United States and Canada. Typically, the procedures used in developing and finalizing these models have involved both expert working groups and opportunities for input from interested parties. What has been missing, however, are empirical data to determine the degree to which the model reflects the views of members of the profession as a whole. METHOD: Using survey data from 466 licensed or registered psychologists (approximately half of whom completed one of two versions of the survey), we examined the degree to which psychologists, both those engaged primarily in practice and those involved in doctoral training, agreed with the competency framework developed by the Association of State and Provincial Psychology Boards' Practice Analysis Task Force (Rodolfa et al., 2013). RESULTS: When distinct time points in training and licensure or registration were considered (i.e., entry-level supervised practice in practicum settings, advanced-level supervised practice during internship, entry level independent practice, and advanced practice), there was limited agreement by survey respondents with the competency framework's proposal about when specific competencies should be attained. In contrast, greater agreement was evident by respondents with the competency framework when the reference point was focused on entry to independent practice (i.e., the competencies necessary for licensure or registration). CONCLUSION: We discuss the implications of these findings for the development of competency models, as well as for the implementation of competency requirements in both licensure or registration and training contexts.
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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 | 0.062 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.005 | 0.026 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".