Psychological Fitness for Deployment: Personality as a Predictor of Performance
Bibliographic record
Abstract
Emergency Response Unit (ERU) that can be applied to a domestic context in Canada.Background: The Canadian Red Cross has extensive experience working in international settings, both in disaster and development.The ERU of the CRC is a health emergency unit that can respond to international humanitarian disasters, either in the form of basic primary care health services or as a field hospital.Much institutional and individual knowledge and skills have been obtained over years of working in these contexts.We hypothesize that there is a large amount of knowledge and skills that have been learned, that could easily be applied to the Canadian domestic context, but this has never formally been studied.Methods: Qualitative methodology: Key informant and semi-structured in-depth interviews.Aim for a diversity of perspectives and in-depth accounts.Sampling and recruitment: Purposive/snowball sampling strategies.Participants will include a diversity of professional backgrounds, ERU HCPs (nurses, physicians, mental health, surgeons, anesthetists), ERU team/ deputy leaders, logisticians, technicians, security managers, and other CRC managers/directors involved in the deployment of health ERU.Data collection: Experienced research assistants will conduct the interviews by Skype, telephone, or in person.Interviews will be audio-recorded (with consent) and are expected to last 30-45 minutes.All interviews will be transcribed.Demographic information: age range, gender, number of years working in humanitarian settings, role collected.Results: Analysis: Three team members will independently code the interviews based on a pre-developed code sheet.Key overarching themes developed.Results: Will be discussed in terms of themes/lessons learned.Discussion will include next steps for integrating this knowledge at the domestic level.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".