Bibliographic record
Abstract
Psychological practice in rural and remote settings involves several unique personal and professional challenges. Generally, very few psychology trainees are formally prepared for those challenges because curriculum components and supervised practice experiences tend to be focused on urban and metropolitan settings. Perhaps not surprisingly, historical shortages of specialty mental health professionals are a persistent problem in most rural communities (DeLeon et al ., 2003). For example, in Australia, one quarter of the population lives in rural and remote areas (Harvey & Hodgson, 1995), but only about 12 percent of all Australian psychologists live and work in those areas (Griffiths & Kenardy, 1996). Mental health services in rural communities There is no consensus regarding the definitions of rural or remote as opposed to metropolitan , but a common characteristic of rural and remote communities is that they are descriptive of areas where the population density is low (U.S. Census Bureau, 2002) and geographic distance imposes restriction upon accessibility to the widest range of goods and services and opportunities for social interaction (Australian Bureau of Statistics, 2001). Long distances and harsh environmental conditions constitute significant barriers for rural residents to access mental health services (DeLeon et al ., 2003). Likewise, delivering services to where they are needed by consumers can be a daunting routine if a home visit by a psychologist means driving several hundred kilometers (Lichte, 1996).
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.006 |
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".