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203 results for “mentoring”
Fig. 28 in Compilation of personal tributes to William Roy Branch (1946-2018): a loving husband and father, a good friend, and a mentor
Fig. 28. Bill Branch and others at Bagamoyo, Tanzania, in 2014.
Fig. 24 in Compilation of personal tributes to William Roy Branch (1946-2018): a loving husband and father, a good friend, and a mentor
Fig. 24. Bill Branch in Angola with a dead on the road Vine Snake (Photo: Alex Paullin).
Fig. 22 in Compilation of personal tributes to William Roy Branch (1946-2018): a loving husband and father, a good friend, and a mentor
Fig. 22. Bill Branch in Angola looking for frogs (Photo: Alex Paullin).
Fig. 15 in Compilation of personal tributes to William Roy Branch (1946-2018): a loving husband and father, a good friend, and a mentor
Fig. 15. Bill Branch and Krystal Tolley in south-western Angola in 2009 (Photo: Krystal Tolley).
Fig. 26 in Compilation of personal tributes to William Roy Branch (1946-2018): a loving husband and father, a good friend, and a mentor
Fig. 26. Bill Branch photographing some lilies at Lake Langanao, Ethiopia (Photo: Steven Spawls).
Fig. 32. Bill with a in Compilation of personal tributes to William Roy Branch (1946-2018): a loving husband and father, a good friend, and a mentor
Fig. 32. Bill with a shoftshell terrapin he caught (Photo: Craig Weatherby).
Fig. 31 in Compilation of personal tributes to William Roy Branch (1946-2018): a loving husband and father, a good friend, and a mentor
Fig. 31. Bill reading science in 45 °C heat (Photo: Amber Jackson).
Mentor Recommendation
<h2>Dataset Description: IT Mentor-Mentee Matching Dataset</h2> <h3>Overview</h3> <p>The <strong>IT Mentor-Mentee Matching Dataset</strong> is a comprehensive collection of profiles designed to facilitate mentor-mentee relationships within the information technology sector. This dataset is based on information gathered from a nonprofit organization in the U.S. that focuses on mentorship initiatives aimed at empowering individuals in the IT field.</p> <h3>Structure</h3> <p>The dataset comprises <strong>5,000 rows</strong> of profiles, each characterized by the following columns:</p> <ul> <li><strong>User_ID</strong>: Unique identifier for each mentor or mentee.</li> <li><strong>Position</strong>: The job title (e.g., Front-End Developer, Data Scientist) relevant to the user.</li> <li><strong>Experience_Level</strong>: A categorical variable indicating the user’s level of experience (e.g., Entry, Mid, Senior).</li> <li><strong>Years_of_Experience</strong>: Numeric representation of the years spent in the industry.</li> <li><strong>Primary_Language</strong>: The primary programming or scripting language the user is proficient in (e.g., Python, JavaScript).</li> <li><strong>Secondary_Languages</strong>: A list of additional programming languages the user is familiar with.</li> <li><strong>Expert_Roles</strong>: Key areas of expertise within the user’s field (e.g., React, Machine Learning).</li> <li><strong>Industry</strong>: The industry context in which the user operates (e.g., Finance, Healthcare, Education).</li> <li><strong>Education</strong>: The highest level of education attained by the user.</li> <li><strong>Availability</strong>: The user’s availability for mentoring or mentoring sessions (e.g., Part-time, Full-time).</li> </ul> <h3>Purpose</h3> <p>This dataset aims to support research and development in mentor-mentee matching systems, enabling organizations, educational institutions, and individual professionals to foster mentorship opportunities that enhance skills and career development within the IT industry. The dataset serves as a valuable resource for various stakeholders seeking to promote growth and knowledge sharing in the tech community.</p> <h3>Use Cases</h3> <ul> <li><strong>Mentor-Mentee Matching</strong>: Create algorithms to effectively match mentors with mentees based on skills, experience, and availability.</li> <li><strong>Collaborative Filtering</strong>: Implement machine learning models that predict compatibility scores between potential mentors and mentees.</li> <li><strong>Data Analysis</strong>: Conduct exploratory data analysis to uncover trends in skills and experience levels within the IT workforce.</li> </ul>
Mentoring Up De-Identified Dataset
<p>We provide here the de-identified data used to evaluate the implementations of a Mentoring Up training. This dataset is currently being used in the publishing of a manuscript, "<i>Mentoring Up for Early Career Investigators</i>: Empowering Mentees to Proactively Engage in their Mentoring Relationships"</p><p>Variables include,</p><p>Values for average self-ratings of Mentoring Competencies using the MCA scale (Likert-type scale from 1 - 7), retrospectively thinking to BEFORE the training and AFTER the training. Subscales include:</p><ul><li>Effective Communication</li><li>Aligning Expectations</li><li>Assessing Understanding</li><li>Fostering Independence</li><li>Addressing Diversity</li><li>Promoting Professional Development</li></ul><p>Values also include responses to the following quantitative questions:</p><ul><li>Overall, how effective was the facilitator in guiding discussion during the workshop? (1 - Very Ineffective, 2 - Ineffective, 3 - Neither Effective nor Ineffective, 4 - Effective, 5 - Very Effective)</li><li>Was attending this workshop a valuable use of your time? (1 - Yes, 2 - No)</li><li>How likely are you to recommend this workshop to other colleagues? (1 - Very Unlikely, 2 - Unlikely, 3 - Undecided, 4 - Likely, 5 - Very Likely)</li><li>Have you made, or do you plan to make any changes in your own practices as a mentee or in your relationship with your mentor(s) as a result of this training? (1 - Yes, 2 - No)</li></ul><p>And the following short answer responses:</p><ul><li>Please describe any changes you plan to make as a result of this workshop</li><li>What aspects of this workshop did you find most useful?</li><li>What could be done to improve this workshop</li><li>What are the strengths and weaknesses of this training? Are there things you would like to see changed or added? If so, what?</li></ul><p> </p>
PEERS Using Peer Mentors to Deliver Depression Care
ClinicalTrials.gov study NCT04319094. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Mentor Siltex® Contour Profile Gel Mammary Prosthesis Clinical Trial
ClinicalTrials.gov study NCT00812097. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Effect of Mentoring on Endotracheal Tube Exchange Using a New Device
ClinicalTrials.gov study NCT02763189. IPD Sharing: YES. Countries: 1. Publications: 1.
E-Mentoring Program for Newly Hired Nurses
ClinicalTrials.gov study NCT06739083. IPD Sharing: NO. Countries: 1. Publications: 5.
Randomized Controlled Trial of Big Brothers Big Sisters Mentoring for Prevention of Crime and Delinquency
ClinicalTrials.gov study NCT03495635. IPD Sharing: YES. Countries: 1. Publications: 9.
A Skills-based RCT for Physical Activity Using Peer Mentors
ClinicalTrials.gov study NCT02329262. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Peer Mentoring to Improve Self-management in Youth With IBD
ClinicalTrials.gov study NCT03827109. IPD Sharing: YES. Countries: 1. Publications: 1.
RENEW Scleroderma: A Peer-Mentored, Web Intervention for Resilience-based, Energy Management to Enhance Wellbeing and Fatigue
ClinicalTrials.gov study NCT04908943. IPD Sharing: YES. Countries: 1. Publications: 4.
A Pilot Peer Mentor Intervention That Trains Black Men Who Have Sex With Men (BMSM) to Use and Promote Uptake of HIV/STI Self-Testing to Peers and Sex Partners: STAR Study (Self-Testing at Your Reside
ClinicalTrials.gov study NCT04121962. IPD Sharing: YES. Countries: 1. Publications: 1.
Using Peer Mentors to Support PACT Team Efforts to Improve Diabetes Control
ClinicalTrials.gov study NCT01651117. IPD Sharing: YES. Countries: 1. Publications: 1.
Core Gel Study of the Safety and Effectiveness of Mentor Round Low Bleed Silicone Gel-filled Mammary Prostheses
ClinicalTrials.gov study NCT00753922. IPD Sharing: Not stated. Countries: 1. Publications: 2.
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