| Literature DB >> 35578284 |
Fatima Tsiouris1,2, Kieran Hartsough3, Michelle Poimboeuf4, Claire Raether3, Mansoor Farahani3, Thais Ferreira3, Collins Kamanzi3, Joana Maria3, Majoric Nshimirimana3, Job Mwanza3, Amon Njenga3, Doris Odera3, Lyson Tenthani3, Onyekachi Ukaejiofo3, Debrah Vambe3, Erika Fazito3, Leena Patel5, Christopher Lee5, Susan Michaels-Strasser3, Miriam Rabkin3.
Abstract
BACKGROUND: The global spread of the SARS-CoV-2 virus highlights both the importance of frontline healthcare workers (HCW) in pandemic response and their heightened vulnerability during infectious disease outbreaks. Adequate preparation, including the development of human resources for health (HRH) is essential to an effective response. ICAP at Columbia University (ICAP) partnered with Resolve to Save Lives and MOHs to design an emergency training initiative for frontline HCW in 11 African countries, using a competency-based backward-design approach and tailoring training delivery and health facility selection based on country context, location and known COVID-19 community transmission.Entities:
Keywords: Africa; COVID-19 training; Competency-based training; Curriculum development; HRH Development; Health workforce; IPC; Virtual training
Mesh:
Year: 2022 PMID: 35578284 PMCID: PMC9109425 DOI: 10.1186/s12960-022-00739-8
Source DB: PubMed Journal: Hum Resour Health ISSN: 1478-4491
COVID-19 training package
| Domain | Competency |
|---|---|
| 1. COVID-19 Origins | HCW can recognize key characteristics of the novel coronavirus and can describe their country’s COVID-19 epidemic stage |
| 2. Disease transmission | HCW can recognize the signs and symptoms of COVID-19 and can list the criteria for suspected, probable, and confirmed cases. They can test suspected cases following national guidelines and report confirmed cases using national reporting tools and platforms |
| 3a. Infection prevention and control (IPC) overview | HCW can ensure adequate IPC including prevention of infections among HCW and nosocomial transmission of COVID-19 within health facilities. Healthcare workers understand IPC principles and practices and how they are applied to the COVID-19 situation |
| 3b. Standard and transmission precautions | HCW can implement standard and transmission-based precautions as per facility standards and guidelines |
| 3c. Implementation strategies | HCW can describe and implement environmental controls to minimize the spread of COVID-19 within health facilities |
| 3d. IPC and WASH (water, sanitation and hygiene) | HCW can describe and support administrative controls to minimize the spread of COVID-19 within health facilities |
| 3e. Personal protective equipment (PPE) | HCW can describe and implement correct use of personal protective equipment (PPE) to minimize the spread of COVID-19 within health facilities |
| 4. Triage of COVID-19 patients | HCW can apply knowledge of national screening guidelines to conduct effective triage, including risk stratification, isolation, and patient referral. (From |
| 5. Maintenance of essential services | HCW can understand the impact of COVID-19 and the COVID-19 response on essential health services and can support new health facility protocols to maintain services during the pandemic |
| 6. Effective communication: dispelling myths | HCW can effectively communicate with patients, community member, facility managers and other stakeholders to disseminate key messages on COVID-19 disease including signs and symptoms, when to seek care at a health facility as well as respiratory and hand hygiene |
Fig. 1Country training locations
Comparison of participant scores in the pre- and post-test and proportions with a passing grade at the 70% cutoff by country
| Country | Mean ± SD | 95% CI | Pre-test pass ≥ 70% | Post-test pass ≥ 70% | ||||
|---|---|---|---|---|---|---|---|---|
| Pre-test | Post-test | Increase | ||||||
| Angola | 436 | 0.57 ± 0.10 | 0.68 ± 0.10 | 0.11 ± 0.09 | (0.10, 0.12) | 25.02 | 56 (13) | 212 (49) |
| Burundi | 74 | 0.27 ± 0.11 | 0.69 ± 0.10 | 0.42 ± 0.12 | (0.39, 0.45) | 29.76 | 0 (0) | 34 (46) |
| Kenya | 350 | 0.72 ± 0.14 | 0.89 ± 0.07 | 0.17 ± 0.15 | (0.16, 0.19) | 22.22 | 203 (58) | 350 (100) |
| Lesotho | 372 | 0.59 ± 0.14 | 0.69 ± 0.12 | 0.10 ± 0.10 | (0.09, 0.11) | 18.85 | 99 (27) | 199 (53) |
| Malawi | 339 | 0.56 ± 0.11 | 0.74 ± 0.11 | 0.18 ± 0.12 | (0.17, 0.19) | 27.87 | 35 (10) | 235 (69) |
| Rwanda | 409 | 0.59 ± 0.11 | 0.73 ± 0.10 | 0.13 ± 0.12 | (0.12, 0.15) | 22.97 | 54 (10) | 230 (56) |
| Sierra Leone | 306 | 0.54 ± 0.14 | 0.70 ± 0.13 | 0.16 ± 0.12 | (0.15, 0.18) | 23.88 | 31 (10) | 176 (58) |
| Zambia | 84 | 0.62 ± 0.12 | 0.70 ± 0.10 | 0.09 ± 0.13 | (0.06, 0.11) | 6.03 | 27 (32) | 45 (54) |
| Total | 2370 | 0.59 ± 0.15 | 0.73 ± 0.13 | 0.15 ± 0.13 | (0.14, 0.13) | 55.09 | 505 (21) | 1481 (62) |
Results are expressed as mean and standard deviation of scores obtained in pre- and post-tests. Significance obtained using paired t-test
SD standard deviation
*All paired t-tests were highly statistically significant at a p-value of < 0.0001Kenya’s pre- and post-tests were distributed by module, not as a singular cumulative test post-exposure to all modules
Overview of training, assessment and survey data demographic characteristics
| Demographics | Training | Pre/post-assessments | Post-training survey |
|---|---|---|---|
| Sex | |||
| Female | 5017 (57) | 1307 (55) | 851 (48) |
| Male | 3778 (43) | 603 (25) | 915 (52) |
| Other | 0 (0) | 0 (0) | 2 (< 1) |
| Missing | 2 (< 1) | 460 (19) | 0 (0) |
| Cadre | |||
| Doctor | 712 (8) | 229 (10) | 126 (7) |
| Non-clinical | 591 (7) | 134 (6) | 133 (8) |
| Nurse | 4950 (56) | 857 (36) | 969 (55) |
| Other HCW | 2041 (23) | 399 (17) | 485 (27) |
| Community HCW | 247 (3) | 0 (0) | 0 (0) |
| Other (IPb, MOH) | 250 (3) | 0 (0) | 55 (3) |
| Missing | 3 (< 1) | 751 (32) | 0 (0) |
| Facility | |||
| Primary | 3110 (35) | 1314 (55) | 698 (40) |
| Secondary | 4995 (57) | 653 (28) | 691 (39) |
| Missing | 31 (< 1) | 403 (17) | 0 (0) |
| N/A/other | 661 (8) | 0 (0) | 379 (21) |
| Country | |||
| Angola | 470 (5) | 436 (18) | 15 (1) |
| Burundi | 398 (4) | 74 (3) | 229 (13) |
| Eswatini | 1812 (19) | 0 (0) | 127 (7) |
| Kenya | 1261 (13) | 350 (15) | 302 (17) |
| Lesotho | 799 (8) | 372 (16) | 115 (6) |
| Malawi | 717 (7) | 339 (14) | 314 (18) |
| Mozambique | 1291 (13) | 0 (0) | 79 (4) |
| Rwanda | 418 (4) | 409 (17) | 287 (16) |
| Sierra Leone | 348 (4) | 306 (13) | 4 (< 1) |
| South Sudan | 804 (8) | 0 (0) | 200 (11) |
| Zambia | 479 (5) | 84 (4) | 92 (5) |
| Missing | 0 (0) | 0 (0) | 4 (< 1) |
aPercent listed represents column percent
bIP = other implementing partners
Comparison of participant pre- and post-test pass/fail proportions at the 70% cutoff by cadre and sex
| Characteristic | Mean ± SD | Pre-test pass ≥ 70% | Post-test pass ≥ 70% | ||
|---|---|---|---|---|---|
| Pre-test | Post-test | ||||
| Cadreb | |||||
| Doctor | 229 | 0.64 ± 0.14 | 0.81 ± 0.11 | 90 (39) | 193 (84) |
| Non-clinical HCW | 134 | 0.51 ± 0.14 | 0.64 ± 0.14 | 11 (8) | 44 (33) |
| Nurse | 857 | 0.72 ± 0.14 | 0.69 ± 0.10 | 204 (24) | 559 (65) |
| Other HCW | 399 | 0.59 ± 0.14 | 0.69 ± 0.12 | 101 (25) | 256 (64) |
| Sexc | |||||
| Female | 1307 | 0.58 ± 0.15 | 0.73 ± 0.13 | 277 (21) | 823 (63) |
| Male | 603 | 0.59 ± 0.16 | 0.75 ± 0.14 | 159 (26) | 400 (66) |
Results are expressed as mean and standard deviation of scores obtained in pre- and post-tests
aRepresents N for those with paired pre- and post-test and demographic data for selected characteristics. Percent listed represents row percent
b751 missing cadre information
c460 missing sex information
Fig. 2Respondent ratings of abilities post-training