Transmission in interprofessional humanitarian teamwork: a scoping review
Bibliographic record
Abstract
Abstract Context Since the end of the 1990s, the humanitarian sector lives a process of professionalization which has created some challenges for organizations and workers. These changes, which aim to standardize and harmonize practices, have also added issues of worker retention, creation of intercultural and interprofessional teamwork, skills development, health, and maintaining expertise in the fields. Aim Identify the mechanisms of knowledge transmission within humanitarian intervention collectives in a professionalization context. Method The design follows the five-step scoping analysis method proposed by Arksey and O’Malley (Int J Soc Res Methodol 8:19-32, 2005). Results The study examines dynamics within humanitarian work groups, focusing on relations between international and national workers, professional segmentation, and collective resilience. It also explores essential knowledge in humanitarian work (contextual, technical, collaborative, metacognitive, and managerial) and the main modes of knowledge transmission: reflective practices, on-the-job transmission, and training. Understanding these interactions and transmissions optimizes effectiveness and skill development in humanitarian contexts. Discussion/conclusion This article discusses professionalization through the rationalization of the humanitarian sector. There is a lack of scientific interest in knowledge transmission through work groups, with a focus on training programme evaluation. Existing dynamics on the ground is harder to control, hindering the evaluation of investment effectiveness. This trend is accompanied by a scarcity of field studies. In addition, there is a need for collective adaptation to respond to unusual situations. However, professionalization, subject to the constraints of donors, can create difficulties for non-governmental organizations (NGO) to establish conditions conducive to resilience or innovation. Our findings call on policy-makers to support adaptable frameworks that improve knowledge sharing and team dynamics in humanitarian response.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.025 | 0.022 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".