Improving Mentorship and Supervision during COVID-19 to Reduce Graduate Student Anxiety and Depression Aided by an Online Commercial Platform Narrative Research Group
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Before COVID-19, post-secondary learning was dominated by in-person, institution-organized meetings. With the 12 March 2020 lockdown, learning became virtual, largely dependent on commercial online platforms. Already more likely to experience anxiety and depression in relation to their research work, perhaps no students have endured more regarding the limitations imposed by COVID-19 than graduate students concerning their mentorship and supervision. The increase in mental health issues facing graduate students has been recognized by post-secondary institutions. Programs have been devised to reduce these challenges. However, the additional attention and funds to combat depression and anxiety have not shown anticipated results. A new approach to mitigate anxiety and depression in graduate students through mentorship and supervision is warranted. Offered here is an award-winning model featuring self-directed learning in a community formed by adding together different, equal, diverse points of view rather than agreement. The approach, delivered through a commercial online platform, is non-hierarchical, and based in narrative research. The proposed model and approach are presented, discussed and limitations considered. They are offered as a promising solution to ebb the increase in anxiety and depression in graduate students—particularly in response to COVID-19.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it