How general practitioners perceive access needs of vulnerable patients and act to address these needs: a mixed-methods study in south-east Melbourne, Australia
Bibliographic record
Abstract
Objective The aim of this study was to understand primary health care (PHC) access barriers for vulnerable people living in south-east Melbourne from the perspective of general practitioners (GPs) who work in the area and to outline strategies that GPs have used to address these barriers. Methods A convergent mixed-methods design was used. Quantitative surveys were conducted with practice managers and GPs, and semistructured qualitative interviews were undertaken with GPs. Data were analysed using a thematic framework approach. Results Each of the vulnerable groups frequently seen by GPs in south-east Melbourne is thought to encounter access barriers in one or more access domains. GPs reported: (1) improving transparency, outreach and information on available treatments to address limited health literacy; (2) using culturally sensitive and language-speaking staff to overcome cultural stereotypes; (3) making practice-level arrangements to overcome limited mobility and social isolation; (4) bulk billing and helping find affordable services to overcome financial hardship; and (5) building trusting relationships with vulnerable patients to improve their engagement with treatment. Conclusion GPs understand the nature of access barriers for local vulnerable groups and have the potential to improve equitable access to primary health care. GPs need support in the on-going application and further development of strategies to accommodate access needs of vulnerable patients. What is known about the topic? Access to primary health care (PHC) is integral to reducing gaps in health outcomes for vulnerable groups. Vulnerable groups often encounter challenges in accessing PHC, and GPs have the potential to improve PHC access. What does this paper add? GPs thought that the vulnerable patients they frequently treat encounter barriers pertaining to both patient access abilities and service accessibility. They reported addressing these barriers by improving transparency, outreach and information on available treatments; using culturally sensitive and multilingual staff; making practice-level arrangements to overcome limited mobility and social isolation; bulk billing and helping find affordable services; and building trusting relationships with vulnerable patients. What are the implications for practitioners? Understanding the nature of access barriers for local vulnerable groups and information on strategies used by GPs allows for the further development of PHC access strategies.
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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".