Skill mix changes in healthcare professions during the COVID-19 pandemic: a scoping review
Bibliographic record
Abstract
OBJECTIVES: The objective of the scoping review was to systematise the existing knowledge about skill mix changes among the healthcare workforce during the COVID-19 pandemic. DESIGN: Scoping review according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Scoping Review. DATA SOURCES: Five databases including CINAHL Ultimate, Web of Science, Medline, Embase and Scopus were searched in August 2024. ELIGIBILITY CRITERIA: The review encompassed original research studies published from January 2020 to August 2024, on the skill mix of healthcare workers during the COVID-19 pandemic. Quantitative and qualitative studies were included without geographical or linguistic restrictions. DATA EXTRACTION AND SYNTHESIS: Data were independently extracted by two researchers, capturing details such as publication year, study title, country, target population, study purpose and methodology, sample size, analysed variables, results and recommendations. RESULTS: A total of 13 563 records were identified in the databases of which 3962 remained for abstract review. 32 articles were included in the final analysis. 17 of the 32 papers were from Western and Southern Europe. The healthcare professions which were described in the studies were physicians, nurses, midwives, paramedics, pharmacists, physiotherapists, occupational therapists and medical assistants, of which the majority of the studies were conducted among nurses (n=16), pharmacists (n=11) and physicians (n=6). Most studies (n=9) concerned the adding of new tasks/roles and reallocating tasks in combination with teamwork (n=8). Research covered a range of topics, including psychological aspects of work, patient safety, work reorganisation, training and collaboration. Many studies focused on the challenges related to skill mix, such as the blurring of responsibilities and role ambiguity. CONCLUSIONS: The research summarised in this review demonstrates the impact of implementing skill mix changes on healthcare workers during the COVID-19 pandemic, particularly in the area of mental health. The research highlights the importance of adaptation in response to pressures among healthcare professions and the entire system. Further research is needed to examine the long-term impact of skill mix on healthcare workers across regions and professions in crisis situations.
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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 | 0.029 | 0.116 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.017 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".