Perceived Barriers to Accessing Specialized Medical Care in Rural Communities of Ontario: A Pilot Study
Bibliographic record
Abstract
Background. Rural residents seeking health care face barriers due to a shortage of healthcare professionals and the travel distance required to obtain medical services. This can lead to potentially harmful health outcomes, particularly when these citizens are unable to access specialized medical care. Few studies have specifically evaluated rural residents’ ability to access medical specialists. Methods. A pilot study was conducted to examine rural residents’ ability to access specialized care. This quantitative pen and paper survey was implemented in two communities with similar health care infrastructure (Tweed, Ontario and Hensall, Ontario). Findings. The majority of respondents (75.8% n= 72 in Tweed and 77.8% n=77 in Hensall;) had received a referral to see a medical specialist in the last five years (total of 352 referrals), which necessitated travelling beyond their communities. Only 5.4% (n=8) of respondents from both communities felt that the travel distance was “too far”. Other important issues identified by respondents included the need for more health services (such as more after-hours access to primary care) as well as the need for better access to medical specialists. Conclusion. Although access to medical specialists in each community is limited, the distance required to access medical specialists in larger centres is not currently perceived to be a barrier to rural residents receiving specialist care. This suggests that barriers to accessing specialist care are surmountable in moderately rural communities and the travel distance to medical specialists is not a significant contributor to poor health outcomes for rural residents.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".