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Project Citation: 

Keralis, Jessica. Global Health Jobs Analysis. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2017-09-04. https://doi.org/10.3886/E100944V1

Persistent URL:  http://doi.org/10.3886/E100944V1

Project Description

Project Title:  Global Health Jobs Analysis
Summary:  Objective
To characterize and gain insight into the global health job market by collecting and analyzing data on global health vacancy postings over a 6-month period.

Summary
This project, a joint effort between the Communications Committee and the Global Health Connections Working Group of the International Health Section, collected data on publicly posted job vacancies with the aim of giving the Section’s students and aspiring global health professionals insight into the current global health job market. As global health graduate programs increase in number and matriculate more students, understanding the range of available jobs and their requirements for applicants is vital for professionals who desire to enter this highly competitive field. 
Data collection was conducted from November 2015 to May 2016. Preliminary findings from the analysis were presented at the 2016 APHA Annual Meeting in Denver, and a presentation on the finalized results has been accepted for presentation at the upcoming meeting in Atlanta. Data collected on job vacancies include employer, position title, location, opening and closing dates, education level required, years of experience required, region and/or technical area of focus, and language skills required or preferred.

For more information, visit 
https://aphaih.org/ghjobs_analysis/.

Scope of Project

Subject Terms:  global health; employment; workforce; job training; labor markets
Geographic Coverage:  United States
Time Period(s):  11/2015 – 5/2016 (November 2015 to May 2016)
Collection Date(s):  11/2015 – 5/2016 (November 2015 to May 2016)
Universe:  Paid jobs in global health open to U.S. citizens and permanent residents
Data Type(s):  aggregate data; other; text
Collection Notes:  In this cross-sectional study, data were collected from global health job vacancies posted to web-based job boards, employment email listservs for global health and development, and global health organizations and consulting firms over a six-month period from November 2015 to May 2016. Vacancy descriptions were analyzed to observe and describe patterns and trends in the global health job market.

Job samples included in the master data set (S1) consisted of one posted job vacancy. A global health vacancy was defined as one soliciting applications for a paid non-internship position working with a project, program, or consultancy focused on a goal directly addressing human health. Positions with development programs not focused on health (e.g., agriculture, animal husbandry, microfinance, democracy/governance, and security), as well as positions only open to nationals from a particular country, were excluded. Both full- and part-time positions were included.

The data set for quantitative analysis (S2) was created by entering information on select characteristics of S1 into a Google Docs workbook saved to the Google Drive of the project Google account. S2 data taken directly from each vacancy description included employer, position title, physical location of position, opening and closing dates (if applicable), education level required and preferred (for private- and nonprofit-sector vacancies), minimum number of years of overall professional experience and directly related experience required (for private- and nonprofit-sector vacancies), lowest and highest GS level (for US federal government vacancies), whether the position is term or permanent (for US federal government vacancies), country or region of focus, whether proficiency in a second (non-English) language was required or preferred, up to two non-English languages desired, and whether overseas experience was required or preferred. For each vacancy, the two predominant areas of technical expertise required for the position were identified. After de-duplication, a unique alphanumeric ID was assigned, and private- and non-profit vacancies were classified by level as practicing clinician (clinical care or training responsibilities specified in vacancy announcement), entry-level (no more than three years of experience and no specialized technical knowledge or experience required), mid-level (two to five years of experience with some specialized technical knowledge or experience required), managerial ("manager" in position title and/or at least three years of project/program management experience required, plus financial, training, strategic planning, operations, or personnel management responsibilities specified in the vacancy announcement), technical/subject matter expert (at least two years of specialized experience required, plus consultative or training responsibilities, or management of technical aspects of projects/programs, specified in the announcement) or directorial ("director," "chief of party", "country representative," or "president" in position title) according to job title, description, and the minimum number of years of experience required. 

Methodology

Sampling: 
The qualitative data set (S3) was generated by simple randomized sampling of S1 without replacement. A sample subset to master dataset percentage of 20% was chosen to avoid both time- and sector-based biases that could have been unintentionally introduced. The chosen method is in line with the premise that a sufficiently large, uniformly randomized sample set allows for analytic generalizability and case-to-case transfer of a job-seeker’s conceptual understanding of job vacancies; specifically, the requirements and qualifications that often categorize job level and function. In addition to simple randomized sampling that was used in the final qualitative analysis, preliminary data that included a non-randomized sample of approximately 25% of the samples collected prior to February 2015 was used as a training and calibration set. This dataset was used to develop a coding methodology and taxonomy for thematic analysis.

S3 were generated from the master data set (S1) by copying the text of each vacancy description (S1) into a plain text editor to remove any formatting and saving the description to the project’s Google Drive as a plain text file. S3 files were saved with the email subject heading of S1 for organizational and reference purposes. 
Data Source:  Vacancies were gathered from twelve internet job boards, including Devex, DevNetJobs, Emory Public Health Employment Connection, Global Health Council, Idealist, Indeed.com (both the global health and international health job boards), Johns Hopkins Bloomberg JHSPHConnect, Peace and Collaborative Development Network, Public Health Institute newsletter, ReliefWeb, and USAJobs. Vacancies were also collected directly from the websites of 20 global health agencies and consulting firms, including Abt Associates, Camris International, CARE, Chemonics, Clinton Health Access Initiative, FHI 360, Gates Foundation, International Medical Corps, International Red Cross/Red Crescent, International Rescue Committee, John Snow International, Management Sciences for Health, Médecins Sans Frontières, PHI Global Health Fellows Program, Population Services International, RTI International, Samaritan's Purse, Save the Children, Task Force for Global Health, U.S. Office of Foreign Disaster Assistance, World Health Organization, and World Vision.
Collection Mode(s):  other


Published Versions

V1 [2017-09-04]

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