Pre-tenures remain survival wise: How to survive your first year in a tenure-track nursing faculty position
Bibliographic record
Abstract
Objective: There are many challenges when starting a nursing tenure track position. This experience exchange paper reflects on a pre-tenured faculty member’s experience during their first year in their position in a university setting.Methods: Tips for surviving the first year of a pre-tenured faculty position using Grylls’ four survival priorities are reflected. Bear Grylls’ advice for outdoor survival adventures can be applied to academia. The survival priorities of Protection, Rescue, Shelter and Water (Food) (or Pre-tenures Remain Survival Wise) are discussed in terms of knowledge gained, development of relationships and the pursuit of opportunities and resources available.Results: It is essential to maintain a positive attitude and learn as much as possible to launch an academic career successfully. Practical tips are explored and exchanged.Conclusions: It is important contemplate in this academic survival scenario, enacting in nursing tenure-track position, the concept of the Darwinian theory of evolution. Reminding us the continued existence of organisms that are best adapted to their environment, with the extinction of others who are not. There is a need to be flexible, resourceful and open-minded when entering academic position.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".