Quality of care provided by mid-level health workers: systematic review and meta-analysis

Lassi, Zohra S; Cometto, Giorgio; Huicho, Luis; Bhutta, Zulfiqar A

doi:10.2471/blt.13.118786

Cited by 73 publications

(52 citation statements)

References 64 publications

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“…In terms of dissatisfiers resulting in workers leaving the job, one study suggested that the inability to retain good health workers resulted in ‘the poor performance of health systems in delivering effective, evidence‐based interventions and that it has been linked to the poor retention, inadequate performance and poor motivation of health workers’ (Lassi et al. , p. 824). Poor systems may create poor quality of care.…”

Section: Discussionmentioning

confidence: 99%

An examination of advanced practice nurses’ job satisfaction internationally

Steinke

Rogers

Lehwaldt

et al. 2017

International Nursing Review

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Section: Discussionmentioning

confidence: 99%

An examination of advanced practice nurses’ job satisfaction internationally

Steinke

Rogers

Lehwaldt

et al. 2017

International Nursing Review

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“…In their systematic review and meta‐analysis of task‐shifting studies, Lassi et al () concluded, “No difference between the effectiveness of care provided by mid‐level health workers and that provided by higher‐level health workers was found.” However, they qualified these findings as being largely based on studies in developed countries, and highlighted a need for more studies conducted in Africa. In the LREC program, task‐shifting was adopted in clinics in Kenya, and data were collected on eligible low‐risk patients.…”

Section: Data Applicationmentioning

confidence: 99%

Causal Inference When Counterfactuals Depend on the Proportion of All Subjects Exposed

2019

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The assumption that no subject's exposure affects another subject's outcome, known as the no-interference assumption, has long held a foundational position in the study of causal inference. However, this assumption may be violated in many settings, and in recent years has been relaxed considerably. Often this has been achieved with either the aid of a known underlying network, or the assumption that the population can be partitioned into separate groups, between which there is no interference, and within which each subject's outcome may be affected by all the other subjects in the group via the proportion exposed (the stratified interference assumption). In this article, we instead consider a complete interference setting, in which each subject affects every other subject's outcome. In particular, we make the stratified interference assumption for a single group consisting of the entire sample. We show that a targeted maximum likelihood estimator for the i.i.d. setting can be used to estimate a class of causal parameters that includes direct effects and overall effects under certain interventions. This estimator remains doubly-robust, semiparametric efficient, and continues to allow for incorporation of machine learning under our model. We conduct a simulation study, and present results from a data application where we study the effect of a nurse-based triage system on the outcomes of patients receiving HIV care in Kenyan health clinics.

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“…But as Padilha et al point out, without high-level political commitment we will not be able to progress beyond piecemeal and short-term approaches and ensure the alignment and coordination of different sectors and constituencies in support of long-term human resource development efforts. 6 Other articles in this theme issue contribute to strengthening the policy frameworks and evidence base surrounding HRH by: (i) helping us to understand the market forces affecting HRH; 7,8 (ii) highlighting best practices and lessons learnt in relation to the retention of health workers in rural areas 9 and the international migration of health workers; 10 (iii) providing new evidence and recommendations on the effectiveness of mid-level 11 and community-based 12 health workers and on the system support they require; and (iv) identifying opportunities for innovation in HRH education and management support through the use of emerging technologies. 13 As countries aspire to achieve UHC, new demands will be placed on health workers.…”

mentioning

confidence: 99%