Narrowing the Skills Gap for Innovation: An Empirical Study in the Hospital Sector
Bibliographic record
Abstract
BACKGROUND: The current financial crisis and the increasing burden of chronic diseases are challenging hospitals to enhance their innovation capacity to deliver new and more effective health services. However, the shortage of skills has been widely recognized as a key obstacle for innovation. Ensuring the presence of a skilled workforce has become a priority for the health system in Portugal and across Europe. OBJECTIVE: The aim of this study was to examine the demand of new skills and their influence in both investments in innovation and development of skills. METHODS: We used a mixed-methods approach combining statistical analysis of data survey and content analysis of semistructured interviews with the Administration Boards of hospitals, using a nominal group technique. RESULTS: The results illustrate an increasing demand of a broad range of skills for innovation development, including responsibility and quality consciousness (with a significant increase of 55%, 52/95), adaptation skills (with an increase of 44%, 42/95) and cooperation and communication skills (with an increase of 55%, 52/95). Investments in the development of skills for innovation are mainly focused on aligning professional training with an organizational strategy (69%, 66/95) as well as collaboration in taskforces (61%, 58/95) and cross-department teams (60%, 57/95). However, the dynamics between the supply and demand of skills for innovation are better explained through a broader perspective of organizational changes towards enhancing learning opportunities and engagement of health professionals to boost innovation. CONCLUSIONS: The results of this study illustrate that hospitals are unlikely to enhance their innovation capacity if they pursue strategies failing to match the skills needed. Within this context, hospitals with high investments in innovation tend to invest more in skills development. The demand of skills and investments in training are influenced by many other factors, including the hospital's strategies, as well as changes in the work organization. Relevant implications for managers and policy makers can be drawn from the empirical findings of this paper, building on the current efforts from leading innovating hospitals that are already defining the future of health care.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".