Exploring knowledge landscapes: A narrative inquiry of midwives’ experiences of working in diverse settings in Ghana
Bibliographic record
Abstract
Objective: This paper focuses on exploring the experiences of midwives in Ghana who have worked in diverse settings over time. It explores how midwives’ personal experiences across time, place and in diverse contexts impact their care for women during childbirth. The paper describes the forms of knowledge held by midwives. It presents how the experiences of midwives reflect their professional and personal practical knowledge landscape.Methods: Using narrative inquiry, the experiences of four midwives working in private maternity homes were explored. Being guided by the three-dimensional narrative inquiry space of temporality, sociality and place, and the concept of relational ethics, a meaningful relationship was built with participants over a period of five months. Several tape-recorded conversations were held with each participant, multiple other interactions were recorded as field notes and in a journal. Each tape-recorded conversation was transcribed and used to construct narrative accounts that reflected participants’ experiences as lived and told. Interim narrative accounts were shared with participants to ensure that the accounts reflected their experiences. Analysis: To identify resonant threads across all four narrative accounts, each account was read multiple times with intentionality and with the research objectives in mind.Results: Three distinct professional knowledge landscapes for midwives were identified. These were the professional knowledge landscape of working in rural communities, urban communities, and private maternity homes. Two concepts of knowledge: knowledge for midwives and midwives’ knowledge, were identified on each of these professional knowledge landscapes.Conclusions: Education of midwives should consciously take into consideration the different knowledge landscapes in which midwives in Ghana practice.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.018 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".