Current professional standing of young medical oncologists in Spain: a nationwide survey by the Spanish Society of Medical Oncology + MIR section
Bibliographic record
Abstract
BACKGROUND: There is a lack of knowledge about the career paths and employment situation of young medical oncologists. The aim of our study was to evaluate the current professional standing of these professionals in Spain. METHODS: The Spanish Society of Medical Oncology + MIR section conducted a national online survey in May 2021 of young medical oncology consultants (< 6 years of expertise) and final year medical oncology residents. RESULTS: A total of 162 responses were eligible for analysis and included participants from 16 autonomous communities; 64% were women, 80% were consultants, and 20% were residents. More than half of the participants performed routine healthcare activity and only 7% research activity. Almost three quarters (73%) were subspecialized in a main area of interest and almost half of these chose this area because it was the only option available after residency. Half of the respondents (51%) considered working abroad and 81% believed the professional standing in Spain was worse than in other countries. After finishing their residency, only 22 were offered a job at their training hospital. Just 16% of participants had a permanent employment contract and 87% were concerned (score of ≥ 5 on a scale of 1-10) about their job stability. In addition, one quarter of the participants in our study showed an interest in increasing their research activity. CONCLUSIONS: The choice of subspecialty in medical oncology may depend on job opportunities after residency rather than personal interest. The abundance of temporary contracts may have influenced the job stability concerns observed. Future mentoring strategies should engage in building a long-term career path for young medical oncologists.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".