Task shifting healthcare services in the post-COVID world: A scoping review
Bibliographic record
Abstract
Abstract Task shifting (TS) redistributes services from specialised to less-qualified providers. Need for TS was intensified during COVID-19 pandemic. We locate evidences of TS across all health conditions to answer: (A) What role has TS played in services delivery since the onset of the pandemic? (B) How has the pandemic impacted strategies of TS globally? We searched five databases in October 2022: Medline, CINHAL Plus, Elsevier, Global Health and Google Scholar. 35 citations were selected. Data was analysed thematically and reported as per PRSIMA-ScR. We used WHO health systems framework and emergent themes to discuss findings. TS was observed in countries of all income-levels. 63% (n=22) articles discussed what impact TS had in COVID-19 care, mental healthcare, care for HIV, sexual and reproductive health, nutrition and rheumatoid diseases. Others (n=13) highlight how pandemic altered TS strategies in mental healthcare, HIV services, hypertension and diabetes and emergency services. Studies varied in reporting TS; majority using terms “task shifting”, followed by “task sharing”, “task shifting and sharing” and “task delegation”. TS affected every block of health system. TS to non-specialists and non-healthcare staff improved services. Modifying roles through training and collaboration strengthened workforce. TS diagnostics increased access to medicines and technologies. Strategic leadership was key. Research on financing TS during pandemics is required. Stakeholders generally accepted TS. Shifting staff between programs led to unintended service incapacities. Pandemic affected strategies of TS. Training, providing care, follow-ups and consultations went digital. Virtually-delivered interventions improved outcomes. Accessibility to digital technology presented barriers. COVID-19 modified health-seeking behaviour. Patients preferred teleconsultations and online-symptom checkers. Organisations altered operating procedures and patient-flow pathways and added precautions to protect staff. Risks of spreading COVID-19 prompted facilities to reconsider TS. TS improved outcomes by filling workforce gaps and increasing access. We recommend TS to improve services delivery during the pandemic and beyond.
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 | 0.008 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.014 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".