Perceived healthcare workforce needs in Lebanon: a step towards informed human resources planning and professional development
Bibliographic record
Abstract
BACKGROUND: Research in various countries has previously investigated the competencies required for effective management of health care organizations. Yet, limited information is available on the skills and knowledge areas, which are currently lacking among the healthcare workforce employed in environments with limited resources. AIMS: The aim of this study was to assess the perceived healthcare workforce needs at the management and clinical/practice levels in Lebanon. METHODS: We conducted an exploratory Delphi study involving two panels of health care professionals and a nationwide survey of hospital directors to assess the skills needed and the healthcare occupations and specialties that are limited. RESULTS: Based on the Delphi study, the top five needed skills/knowledge areas were: professionalism, ethics, quality management and improvement, strategic planning, and communication. The need for information management and technology skills was reported by more than 50% of urban hospitals, and highlighted by the two panels in the Delphi study. Healthcare professionals reported willingness to take continuing education courses. Hospitals further indicated the availability of financial support and willingness to collaborate with educational institutions for employee training and continuing education. CONCLUSIONS: Our findings set the ground for future research investigating healthcare workforce issues in Lebanon and support evidence-based planning for health human resources. They may inform the development of national and local policies in the country, which address the human resources needs of the health care system to meet regional and national demands. Universities, professional syndicates, and nongovernmental organizations may leverage these findings to develop continuing education training and diplomas incorporating the competencies critical for the healthcare workforce.
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 | 0.012 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".