A survey of pediatric neuropsychologists serving inpatient rehabilitation, Part II: billing, time allocation and tracking, and professional identity and perceptions
Bibliographic record
Abstract
Professional challenges have been documented in broad surveys of neuropsychologists. While previous surveyors have included pediatric neuropsychologists, few, if any, have specifically examined practices among those who primarily work in pediatric inpatient rehabilitation settings. Therefore, the aim of this study was to survey neuropsychologists in this setting. Thirty neuropsychologists from the U.S. and one from Canada that work in inpatient pediatric rehabilitation participated in an online survey. Most respondents (83.3%) billed for their inpatient time. Sixty-four percent indicated that payor type (private vs. public) affected services a moderate amount to a lot; this was primarily due to payor's influence on length of stay. Most providers had productivity expectations (66.7%). Among those that had productivity expectations, three-quarters used "hours billed;" 37.5% were solely or additionally tracked by relative value units (RVUs). The majority of respondents conducted some type of clinical data collection, usually for research purposes. With respect to professional identity, most respondents indicated positivity about their role. The top challenges endorsed were related to workload/ability to meet the patients' needs and billing/productivity. Issues related to billing and payor may influence aspects of pediatric inpatient rehabilitation neuropsychological care. Managing challenges related to billing and the time demands associated with providing inpatient services were top concerns for many respondents. Most sites surveyed were involved in data collection, usually for research purposes; increased data collection efforts are needed to aid with program development and evaluation and to demonstrate the added value of neuropsychological services from a patient care perspective.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".