The emerging role of social media platforms in advanced practice provider recruitment: One institution’s experience
Bibliographic record
Abstract
Social media is having a major impact on how today’s society communicates with one another, receives information regarding current events, and professionally networking. With this rapid increase, this project sought to investigate the primary resources that Advanced Practice Providers (APPs) at Texas Children’s Hospital used to find their current job and to determine what resources they would utilize for future positions. In the rapid increase of social media platforms and number of social media users, the recruiting needs may be changing drastically, specifically for job seekers in healthcare. As APPs search for employment, the avenues of seeking and applying for positions differ than they did when social media was not on the rise. This article discusses the progressive needs in the constantly evolving healthcare community in regards to recruiting and how, specifically, the APPs at Texas Children’s Hospital (TCH) searched and applied for their 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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".