Negotiating expectations for therapy between mothers and service providers: a narrative analysis
Bibliographic record
Abstract
To use stories about mothers and service providers negotiating expectations for therapy to illuminate processes contributing to power differences within partnerships. This narrative study presented stories from three mothers and three service providers. Stories were co-constructed between participants and researchers and analyzed using narrative analysis. Building on An and Palisano’s (2014) Model of Family-Professional Collaboration, stories were organized into the stages of goal setting, planning, and doing therapy. Each story illuminated a process unique to that story that can redistribute power between mothers, service providers, and therapy environments while negotiating expectations: protecting sacred issues, facilitating knowledge exposure, filling voids/vacuums, recognizing cultural conditioning, re-discovering eclipsed roles, and connecting relay teams. We propose including three additional strategies to An and Palisano’s model to increase the readiness of mothers and service providers to negotiate expectations for therapy and collaborate fully as the model intends: 1) exploring power-sharing conversations; 2) looking for social context clues; and 3) adopting a humility stance. Information on the six illuminated processes can help structure a client story grounded in optimal negotiation of expectations and equal partnerships.Implications for RehabilitationKnowledge of diverse stories about mothers and service providers negotiating expectations for therapy can be a resource to guide actions in related situations.Leaving the topic of expectations for therapy implicit or unchallenged increases the risks that negotiations remain unbalanced and unproductive.Service providers may enhance collaboration with mothers in paediatric rehabilitation by exploring power-sharing conversations, looking for social context clues, and adopting a humility stance. Knowledge of diverse stories about mothers and service providers negotiating expectations for therapy can be a resource to guide actions in related situations. Leaving the topic of expectations for therapy implicit or unchallenged increases the risks that negotiations remain unbalanced and unproductive. Service providers may enhance collaboration with mothers in paediatric rehabilitation by exploring power-sharing conversations, looking for social context clues, and adopting a humility stance.
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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 | 0.016 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".