Improving caretaker mental welfare through targeted on-farm euthanasia training programs
Bibliographic record
Abstract
Swine employee retention factors are multi-faceted, complicated, inter-twined, and often differ from other businesses. Positive factors of the job include direct pig interaction, career enhancement opportunities and reliable and varied tasks. However, negative factors of the job may include early starts and long hours, weekend shifts, challenging environmental conditions, lower salaries compared to other entry level positions, repetitive work, cultural differences, and tasks that are mentally challenging. In 2022, 17.8% of workers employed in animal production and aquaculture were Hispanic and/or Latino from South American countries. The North American Free Trade Agreement (NAFTA; TN) visa which “allows qualified Canadian and Mexican citizens to seek temporary entry into the United States to engage in business activities at a professional level.” This gives some foreign workers the opportunity to seek employment in the U.S. These caretakers on pig farms are front line that have to engage in challenging yet highly important tasks to ensure pig welfare. One of these tasks is timely euthanasia. Previous studies suggest that caretakers performing euthanasia are at an increased risk of emotional mismanagement, physical ailments, unresolved grief, depression, substance abuse, and even suicide. Improved and supported caretaker psychological welfare can be met through focused and relevant training that are based around personality and learning style preference. One personality assessment tool is the Predictive Index (PI) Behavioral Assessment. This is an untimed, free-choice, self-report tool based on two questions. Question one asked caretakers to select as many words from a provided list that they thought represented how others expect them to act. The second question asked caretakers to select as many words from a provided list on how they identified themselves. These selected words are automatically categorized by the PI algorithm software and a caretaker is allocated a behavioral profile based on dominance, extraversion, patience, formality, and objectivity. There is no published literature using the PI Behavioral Assessment to (a) identify a swine caretakers’ PI profile, (b) allocate them to a specifically designed on-boarding swine euthanasia training and, (c) to determine if the PI Behavioral Assessment positively supports their psychological welfare. Therefore, the overall goal of this thesis was to improve caretaker mental welfare through targeted on-farm euthanasia training programs. The specific objective to meet this aim was to determine if a tailored swine euthanasia training tool based on the PI Behavioral Assessment improves caretaker euthanasia attitudes, perceptions and supports caretaker mental welfare. Swine euthanasia training modules based on the PI Behavioral Assessment and surveys were created and tested on TN-visa holding swine caretakers employed on commercial sow farms. Our data indicates that Mexican caretakers on TN visas have positive perceptions (P 0.0034) and are decisive (P 0.0001). In addition, they are knowledgeable in diagnosing and performing euthanasia when a pig or piglet get sick or is compromised (P 0.0045). These caretakers are compassionate but do not feel bad about euthanizing pigs or piglets (P 0.0001). Overall, caretakers saw value in both the PI Behavioral Assessment combined with the on-farm euthanasia training (P 0.15). In conclusion, the PI Behavioral Assessment tool combined with a specialized euthanasia module training that was designed around learning, demonstrated value in supporting TN caretaker mental welfare. It is recommended that this tool and specialized module training be integrated into swine euthanasia training.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".