Bibliographic record
Abstract
Purpose The purpose of this paper is to explore three sociocultural themes common to migrant and seasonal farmworkers and to demonstrate the value of incorporating oral history into healthcare practice and quantitative, qualitative, or mixed-methods research programs, as oral history is a culturally sensitive approach to working with vulnerable populations. Design/methodology/approach This paper examines 17 oral histories from farmworkers residing in Ottawa County, Michigan, in the late summer of 2014. The theoretical framework section has two aims. First, it explains the significance of “cultural sensitivity” and “deep structure” to the practice of effective healthcare. Second, it introduces oral history as a form of deep structure cultural sensitivity. Findings Three themes emerge from the collected oral histories: stress/anxiety of undocumented status, honor/worth of honest work, and the importance of educating migrant children. Undocumented status is found to be the hub of farmworker health inequities while worth of work and education are described as culturally sensitive points of conversation for healthcare workers engaging with this population. Finally, oral history is found to be a useful method for establishing the deep structure of cultural sensitivity. Originality/value This paper gives a voice to farmworkers, an inconspicuous population that disproportionately suffers from health inequities. In addition, this paper acts as a case study promoting the use of oral history as a novel, culturally sensitive research method.
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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 | 0.001 | 0.001 |
| 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 | 0.000 | 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".