Exploring the Factors That Impact Recruitment and Retention of Pediatricians in Irish Community Hospitals Through the Attitudes of Trainees and Physicians-in-Practice
Bibliographic record
Abstract
BACKGROUND: Difficulty attracting physicians to work in rural and remote areas is a worldwide problem. Specific to pediatrics, barriers to recruitment include burdensome on-call rosters, a lack of career opportunities for partners and professional isolation. METHODS: To examine attitudes to working in a community hospital in Ireland, a mixed-methods sequential analysis approach was undertaken. Pediatricians-in training (70) and attending community pediatricians (25) completed surveys. Six semistructured interviews were used to triangulate survey results. RESULTS: Most trainees planned to stay in Ireland (66/70), with five (eight%) stating that a career in a community hospital was their first preference. Personal factors such as a partner's career prospects and closeness to family and friends were the most important deterrents to working in a community hospital for trainees. Both trainees and attendings were concerned regarding professional isolation. Trainees were concerned about the poor reputation of community units. This converged with attendings feeling their role was not adequately respected, even though their job had more variability and exposure to emergencies, with less support, than working in a large center. Both groups agreed that targeted postgraduate training pathways and better training opportunities within Ireland were the best way to improve recruitment. Financial bonuses were not highly ranked as potential incentives. Motivators for considering a career in community units included the desire to make an impact and to build something new. Concerns about job satisfaction, professional recognition, and limited support for service development were prevalent. CONCLUSION: This study reveals critical challenges and motivators influencing the shortage of pediatricians in Irish community units. Addressing these issues requires a multifaceted approach, incorporating targeted training, support structures, and recognition to enhance recruitment and retention in these underserved areas. Insights from the Irish context could be applied to improve recruitment and retention of pediatricians in regions with similar contexts.
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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".