Bibliographic record
Abstract
Abstract According to the US Census, more than 16% of Nebraska’s population is 65 and older. Many older Nebraskans reside in rural communities. Although clinicians in existing state-wide workforce surveys endorse working with older adults, it is unclear how frequently they are involved in geriatric mental health. To identify the needs of those who are providing mental health services to older Nebraskans, the Behavioral Health Education Center of Nebraska (BHECN) conducted an online survey of eligible licensed practitioners from August to December 2022. This presentation highlights the background characteristics of 193 respondents, nearly 40% of whom came from rural areas. Two-thirds identified either as licensed mental health therapists (38%), licensed clinical social workers (26%), or licensed psychologists (14%). More than a quarter did not provide direct clinical services to older adults. On average, respondents estimated devoting 18% of their time working with older adults. Respondents listed insurance restrictions, lower reimbursement rates, and limited specialized geriatric mental health training opportunities as major reasons that had kept them from serving older clients more frequently. These findings support BHECN’s ongoing efforts to further develop training and support, and advocate for potential policy changes to enhance the state’s geriatric behavioral and mental health workforce.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".