Evaluation of a Facilitation Skills Training Program to Improve Emotional Intelligence and Leadership Performance of Non-Profit Leaders in Bangladesh
Bibliographic record
Abstract
With relatively few training programs available to non-profit leaders in Bangladesh, it is crucial to develop their emotional intelligence which is linked to effective leadership and superior performance in addressing poverty and inequities faced disproportionately by marginalized communities. Aligned with behavioural leadership theory, mixed models of emotional intelligence and adult learning theory, this study accepts the evidence that emotional intelligence competencies associated with improved leadership can be learned and developed. For this study, a facilitation skills training program for non-profit leaders in Bangladesh was designed, delivered and evaluated. This research analyzed the connections between facilitation skills, emotional intelligence competencies, leadership performance and team performance that emerged from the program intervention and how it influenced non-profit leaders’ readiness to achieve their organizational goals. In Dhaka, Bangladesh, the five-day program intervention was delivered to 17 non-profit leaders on November 12-16, 2022, with a primary focus on developing facilitation skills and offering practice opportunities with continuous feedback. The program intervention underwent a three-phase evaluation: participants completed a pre-test, retrospective pre-test, and post-test using the Emotional Intelligence Competency Self-Assessment (EICSA) survey designed specifically for this study; one-hour video interview using a modified behavioural event interview design; and, an adapted version of the Team Emotional Intelligence Assessment (TEIA). Program participants credit the facilitation skills training program with contributing to the development of emotional intelligence competencies, contributing to positive changes to their leadership performance, team performance and their readiness to achieve their organizational goals. The findings suggest that the participants were capable of consciously developing emotional intelligence competencies, which are empirically tied to improved leadership performance (Finding 1), and it may contribute to achieving organizational goals and addressing inequities in marginalized communities (Finding 2). Furthermore, the facilitation skills training may influence the development of team emotional intelligence norms empirically linked to increased team performance (Finding 3).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".