Open-sourced equity survey to assess organizational diversity and inclusion
Bibliographic record
Abstract
The Intensive Care Unit (ICU) Bridge Program (ICUBP) has developed a 12 minute, bilingual, and optionally anonymous equity and diversity survey to assess the demographics among volunteers and to evaluate equitable practice within the organization. The outcomes of the survey are currently the basis for recruitment initiatives by their executive team. The purpose of this paper is to make the survey open-source to allow for other institutions to use it as a resource and source of information to set the foundation and/or build on their own equity assessment tools and incorporate these into their existing infrastructure. ---------- Le Programme de Liaison de l’Unité de Soins Intensif (PLUSI) vient de développer un 12 minutes, bilingue, et facultativement anonyme sondage d’équité et de la diversité pour évaluer les facteurs démographiques parmi ses bénévoles et pour surveiller les pratiques équitables au sein de l’organisation. Les résultats du sondage sont la base d’initiatives de recrutement par leur équipe exécutive. Nous voulons partager le sondage et comment c’est intégré dans le flux de travail quotidien du programme pour qu’il soit de source ouverte pour permettre les autres institutions à l’incorporer facilement dans leur propre infrastructure d’outils de surveillance d’équité.
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.
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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".