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The learning environment of paediatric interns in South Africa.

ABSTRACT: South African (SA) paediatric interns (recently qualified medical graduates) work in a high disease burdened and resource deficient environment for two years, prior to independent practice. Perceptions of this learning environment (LE) influences their approaches to training as well as the outcomes of this period of development. Obstacles to creating a supportive LE and supervisor interaction affects the quality of this training. Measuring perceptions of the LE with validated instruments can help inform improvements in learning during this crucial period of medical education.The aims of this study was to determine the psychometric qualities of the Postgraduate Hospital Educational Environment Measure (PHEEM) amongst paediatric interns across four hospital complexes in South Africa and to measure the LE as perceived by both interns and their supervisors. Construct validity was tested using factor analysis and internal consistency was measured with Cronbach's alpha.A total of 209 interns and 60 supervisors (69% intern response rate) responded to the questionnaire. The PHEEM was found to be very reliable with an overall Cronbach's alpha of 0.943 and 0.874 for intern and supervisors respectively. Factor analysis using a 3-factor solution accounted for 42% of the variance with the teaching subscale having the best fit compared with the other sub-scales of the original tool. Most interns perceived the learning environment as being more positive than negative however, their perceptions differed significantly from that of their supervisors. Poor infrastructural support from institutions, excessive workloads and inadequate supervision were factors preventing optimal training of paediatric interns.The SA version of the PHEEM tool used was found to be a reliable and valid instrument for use in interns amongst high disease burdened contexts. Various obstacles to creating an ideal learning environment for paediatric interns were identified to be in need of urgent review. Key differences in perceptions of an ideal learning environment between interns and their supervisors need to be fully explored as these may result in sub-optimal supervision and mentoring.

SUBMITTER: Naidoo KL

PROVIDER: S-EPMC5707910 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ObjectivesDelays in identification, resuscitation and referral have been identified as a preventable cause of avoidable severity of illness and mortality in South African children. To address this problem, a machine learning model to predict a compound outcome of death prior to discharge from hospital and/or admission to the PICU was developed. A key aspect of developing machine learning models is the integration of human knowledge in their development. The objective of this study is to describe how this domain knowledge was elicited, including the use of a documented literature search and Delphi procedure.DesignA prospective mixed methodology development study was conducted that included qualitative aspects in the elicitation of domain knowledge, together with descriptive and analytical quantitative and machine learning methodologies.SettingA single centre tertiary hospital providing acute paediatric services.ParticipantsThree paediatric intensivists, six specialist paediatricians and three specialist anaesthesiologists.InterventionsNone.Measurements and main resultsThe literature search identified 154 full-text articles reporting risk factors for mortality in hospitalised children. These factors were most commonly features of specific organ dysfunction. 89 of these publications studied children in lower- and middle-income countries. The Delphi procedure included 12 expert participants and was conducted over 3 rounds. Respondents identified a need to achieve a compromise between model performance, comprehensiveness and veracity and practicality of use. Participants achieved consensus on a range of clinical features associated with severe illness in children. No special investigations were considered for inclusion in the model except point-of-care capillary blood glucose testing. The results were integrated by the researcher and a final list of features was compiled.ConclusionThe elicitation of domain knowledge is important in effective machine learning applications. The documentation of this process enhances rigour in such models and should be reported in publications. A documented literature search, Delphi procedure and the integration of the domain knowledge of the researchers contributed to problem specification and selection of features prior to feature engineering, pre-processing and model development.

Project description:Background. The bacterial foodborne pathogen Campylobacter jejuni is a common cause of acute gastroenteritis and is also associated with the postinfectious neuropathies, Guillain-Barré and Miller Fisher syndromes. This study described the use of multilocus sequence typing and DNA microarrays to examine the genetic content of a collection of South African C. jejuni strains, recovered from patients with enteritis, Guillain-Barré or Miller Fisher syndromes. Methodology/Principal Findings. The comparative genomic analysis by using multilocus sequence typing and DNA microarrays demonstrated that the South African strains with Penner heat-stable (HS) serotype HS:41 were clearly distinct from the other South African strains. Further analysis of the DNA microarray data demonstrated that the serotype HS:41 strains from South African GBS and enteritis patients are highly similar in gene content. Interestingly, the South African HS:41 strains were distinct in gene content when compared to serotype HS:41 strains from other geographical locations due to the presence of genomic islands, referred to as Campylobacter jejuni integrated elements. Only the genomic integrated element CJIE1, a Campylobacter Mu-like prophage, was present in the South African HS:41 strains whereas absent in the closely-related HS:41 strains from Mexico. A more distantly-related HS:41 strain from Canada possessed both genomic integrated elements CJIE1 and CJIE2. Conclusion/Significance. These findings demonstrated that these C. jejuni integrated elements may contribute to the differentiation of closely-related C. jejuni strains. In addition, the presence of bacteriophage-related genes in CJIE1 may probably contribute to increasing the genomic diversity of these C. jejuni strains. This comparative genomic analysis of the foodborne pathogen C. jejuni provides fundamental information that potentially could lead to improved methods for analyzing the epidemiology of disease outbreaks and their sources. Keywords: comparative genomic indexing analysis

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The learning environment of paediatric interns in South Africa.

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