An institutional ethnography inquiry of health care work in special education: a research protocol
Bibliographic record
Abstract
BACKGROUND: Special education for children with chronic health conditions or disabilities requires the integration of health care work with education. This phenomenon occurs in an understudied and challenging context for integrated care despite policies and protocols that outline work processes in this context. We are interested in an approach to inquiry that will allow us to address gaps in current literature and practices in integrated care, and move towards informing policy. STUDY DESIGN AND DATA COLLECTION METHODS: Institutional ethnography is an approach to inquiry that maps the actualities of what individuals do at an everyday local level, while examining this work activity in relation to the sociopolitical context. It has been used to change policy and local practice by highlighting disjunctures between policy and actuality. We are adopting institutional ethnography and its three common methods of data collection: document collection, interviews, and observation/shadowing. Informants to this inquiry are chosen from school-based teams, family-centred units and constellations of clinical professionals. METHODS OF ANALYSIS: We are following work processes, verbally and visually mapping what is done and by whom. It is important to note that work includes 'unofficial' work, including the work of families and others who may not be assigned an official work role in a policy or protocol. The mediating role of texts in work processes is also being mapped in order to link the local work to the high-level social coordinators. To begin, analysis focuses on local, or micro-level, work processes; next, analysis identifies and explains the macro-level coordination of the local work (i.e. social and political structures). CONCLUSION: A primary outcome of this study will be the creation of verbal and visual maps that demonstrate the social organisation of work processes occurring in the health care-special education interface. These maps will make invisible work visible, highlight disjunctures between policy and practice and identify opportunities for change. They will be useful for critical knowledge translation purposes, providing parents and professionals with an awareness of how their individual work fits in to the larger picture of integrating health care work in special education.
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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 | 0.071 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".