Education and Training Needs of Health Care Professionals in the Philippines Encountering Patients with Lung Oligometastatic Cancers
Bibliographic record
Abstract
This study aimed to examine the education and training needs of health care practitioners (HCPs) in the Philippines who encounter lung oligometastatic cancer patients. Lung oligometastatic disease is among the most common sites for cancer spread and has the most established practices for treating oligometastases. A modified version of the Hennessy-Hicks Training Needs Assessment Questionnaire was administered online to HCPs working in private and public centers in the Philippines. HCPs were recruited via purposive sampling. Twenty-seven HCPs completed the questionnaire (47% response rate). Respondents were mostly female (59%) and between the ages of 30 and 39 years (70%). Three-quarters (74%) were consultants, and most respondents were radiation oncologists (44%) or medical oncologists (30%). Medical oncologists rated Management/Supervisory Tasks (mean = 1.42) as their highest area of training need while radiation oncologists rated Clinical Tasks (mean = 1.30) as their highest training need. Pulmonologists (mean = 0.60) and other specialists (mean = 1.00) rated Administration tasks as their top area of training need. The clinical task-related category was rated the highest need among the continuing medical education topics. This study provides valuable insights for the implementation and advancement of a comprehensive curriculum in clinical oncology, specifically designed to enhance the administrative, clinical, and research capacities of oncologists who encounter oligometastatic lung disease in the Philippines.
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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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".