Knock Knock….. Who's there? Supporting well-being and success for all
Bibliographic record
Abstract
Workshop Description The facilitators will introduce the need for supporting learner wellbeing and why it must be a focus. Challenges to learner well-being includes personal and environmental factors, these domains explore the learner journey and transitions, length and challenges of programmers respectively The role of the education providers in promoting well-being will be explored along with evidence based support framework that can be utilised in the learning environment, such as; • general support that is easily accessible, transparent and timely, • preventative support such as activities that help with resilience and personal and professional support sessions and • extra support for the specific needs of the individual, personalised and offered early The facilitators will particularly focus on the roles of faculty and peers in student support; the evidence based staff development framework will explore difficult conversations and provide sufficient tools for delegates to feel confident in the strategies discussed. The likely student concerns that present will be reviewed with activities and role-play including study, relationships, financial, physical illness, mental health. Case-based scenarios will explore how delegates have responded previously and how best to triage with local guidelines and policies in mind. Whilst supporting staff who support students, we can explore: • why staff need training and support too • who should be recruited and selected to these roles • how they should be suitable inducted • the role clarification and expectations • ongoing professional development and • how to deal with emergency situations The session will conclude with how the delegates are currently focusing on their own well-being, with increases stressors and demands of the higher education and work environment. Practical advice will be shared and delegates armed a sense of purpose, meaning and connection. W2P7 Workshop Objective The objective of the workshop is to briefly review the current literature on supporting learner well-being and discuss frameworks and practical approaches for those who educate and may be confronted with and required to respond to distressed learners whilst maintain their own well-being. Who Should Attend Educators (teachers and clinicians), fellow learners, professional staff and those who manage others who are frontline for student support.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.074 | 0.039 |
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".