Construction of a conceptual framework for assessment of health-related quality of life in calves with respiratory disease
Bibliographic record
Abstract
Bovine respiratory disease (BRD) is one of the most prevalent diseases affecting beef and dairy calves worldwide, with implications for lifetime productivity, antimicrobial use and animal welfare. Our objective was to construct a conceptual framework for assessment of health-related quality of life (HRQL) in calves with respiratory disease, based on indicators suitable for direct pen-side visual observation. Health-related quality of life measures aim to evaluate the subjective experience of the animal rather than any related pathology. A conceptual framework graphically represents the concepts to be measured and the potential relationships between them. A multistage, mixed method approach involving diverse data sources, collection methods and stakeholders was applied to promote comprehensiveness, understanding and validity of findings. A scoping review was conducted to identify, characterize and collate evidence of behavioural indicators of BRD. The indicators identified were mapped against the principal attributes of five prominent animal welfare assessment frameworks to appraise their correspondence with different characterizations of the dimensions of welfare. Forty-two semi-structured, individual, qualitative interviews with a purposeful sample of experienced veterinarians and stockpersons from UK, USA and Canada elicited in-depth descriptions of the visual observations of HRQL they make in diagnosing and assessing the response to treatment of calves with BRD. Verbatim interview transcripts were examined using inductive thematic analysis. Respondents provided insights and understanding of indicators of HRQL in BRD such as interaction with feed source, hair coat condition, specific characteristics of eye appearance, eye contact, rumen fill and stretching (pandiculation). In an on-farm pilot study to assess the value of potential HRQL behavioural indicators, there was a moderate positive correlation between behaviour and clinical scores (rs = 0.59) across the 5 days preceding veterinary treatment for BRD. Interestingly, the behaviours evaluated were observed a median of 1.0 (interquartile range: 1.0–3.5) days before clinical indicators used in the scoring system. The proposed conceptual framework for assessment of HRQL features 23 putative indicators of HRQL distributed across two interrelated domains – clinical signs and behavioural expressions of emotional well-being. It has potential applications to inform the development of new HRQL measures such as structured questionnaires and automated sensor technologies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".