Clinical neuropsychology in Canada: results from the 2020 AACN, NAN, SCN professional practice and “salary survey”
Bibliographic record
Abstract
Objective: The current study summarizes the results of a 2020 survey that solicited information regarding backgrounds, beliefs, practices, and incomes of clinical neuropsychologists who practice in Canada. Methods: Clinical neuropsychologists who practice in Canada were invited to participate in an online survey that was available from 1/17/20 to 4/02/20. Available survey findings were obtained from 111 respondents, which reflects a response rate of 51.3% of the 216 doctoral-level Canadian neuropsychologists identified in at least one major North American or international professional organization membership list (AACN, INS, NAN, or SCN). Results: Most of the current respondents were White/Caucasian women who identified as adult providers and worked full-time in urban institutional settings. Four Canadian provinces (Alberta, British Columbia, Ontario, Quebec) accounted for more than 91% of the current respondent sample. Incomes and career satisfactions were largely encouraging, though some important variations were noted by province, work setting, and professional identity. Incomes were significantly associated with forensic practices and years of clinical experience. Most respondents made use of technician support in their practices, largely to increase productivity and patient volume. Only a small minority of respondents were board-certified and there was generally limited interest in future board certification. Conclusions: While important similarities were observed in the current Canadian sample relative to recent survey findings obtained in a U.S. sample, results also reveal a number of important differences that serve as important areas of future consideration.
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.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".