The motivations and experiences of specialists who provide outreach services in rural operating rooms: A survey study from British Columbia
Bibliographic record
Abstract
INTRODUCTION: Outreach care has long been used in Canada to address the lack of access to specialist care in rural settings, but research on the experiences of specialists providing these services is lacking. This descriptive survey study aimed to understand 1) specialists' motivation for engaging in outreach work, (2) their perceptions of the quality of care at their rural outreach hospital, and (3) the supports they receive for their outreach work, in order to create a supportive framework to encourage specialist outreach contributions. METHODS: In July 2022, specialist physicians who provide outreach operating room services at rural hospitals participating in the Rural Surgical and Obstetrical Networks initiative in the province of British Columbia were invited to complete an anonymous survey. RESULTS: 21 of 45 invited outreach specialists completed the survey (47% response rate). Three-quarters of respondents had a surgical specialty. The opportunity to deliver care to underserved patients was the most common motivator for outreach work. Rural hospitals received high ratings from respondents on overall safety and various aspects of communication and teamwork. Postoperative care was a concern for a minority (one-fifth) of respondents, and about half had experienced unnecessary delays between procedures some or most of the time. Generally, respondents felt integrated into rural teams and reported receiving adequate nursing and anesthetic support. The two most common desired additional supports were better/more equipment and space and additional staffing. All 19 respondents not planning to retire soon intended to provide outreach services for at least three more years. CONCLUSION: Specialists providing outreach OR services in small volume rural hospitals in BC usually have altruistic motives for outreach work. For the most part, these specialists have positive experiences in rural hospitals, but they can be better supported through investment in infrastructure and health human resources. Specialists intend to provide outreach services long-term, indicating a stable outreach workforce. More research on the facilitators and barriers of specialist outreach work is needed.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".