Leading Wellbeing Practices in Prime Ministers' Offices
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
This chapter makes a case for the role of political managers to lead positive wellbeing practices in the Prime Minister’s office. To do their job effectively, political leaders need healthy and high-performing political advisors, but the infrastructure around these roles is historically limited and problematic. Drawing on interviews with former political advisors who worked in the UK, Canadian, Australian, and New Zealand Prime Minister’s Offices and using an appreciative inquiry approach, the chapter outlines five effective practices that can employ to support the mental health of advisors: (1) Acknowledge pressures; (2) Check in on colleagues to convey care and detect potential burnout; (3) Enable breaks and sleep; (4) Accommodate personal demands; and (5) Normalise and protect. It also argues that there is a role for psychologists to become engaged in designing and advocating for improvements. Given that Prime Ministers make decisions that affect millions of people, supporting those who advise them will improve the wellbeing of us all.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it