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Nursing Against the Odds: How Health Care Cost Cutting, Media Stereotypes, and Medical Hubris Undermine Nurses and Patient care

2005· article· en· W1976021629 on OpenAlexaboutno aff
Jane Robinson

Bibliographic record

VenueInternational Nursing Review · 2005
Typearticle
Languageen
FieldHealth Professions
TopicHealth and Conflict Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHubrisPoliticsHealth careNursingOddsMedicineSociologyPolitical scienceHistoryLawClassics

Abstract

fetched live from OpenAlex

Nursing Against the Odds: How Health Care Cost Cutting, Media Stereotypes, and Medical Hubris Undermine Nurses and Patient care Suzanne, Gordon , 2005 ; ILR Press, an imprint of Cornell University Press, Ithaca, New York and London : 489 pages. ISBN: 0-8014-3976-0 . Nursing against the Odds should be on every nurse's booklist and is essential reading for anyone remotely interested in the politics of nursing. Suzanne Gordon is an award-winning journalist who has written two previous books on aspects of nursing as well as numerous articles in the American and Canadian press. She is Assistant Adjunct Professor at the University of California at San Francisco School of Nursing and also edits with Siobhan Nelson (Editor of Nursing Inquiry) a series of books on The Culture and Politics of Health Care Work. In addition to this impeccable pedigree, Suzanne Gordon writes in an immensely knowledgeable and readable style. She is up-to-date with all the current issues that preoccupy nurses and she writes about them with deep understanding and lucidity. Perhaps her greatest credit is that she covers all the arguments, both for and against a particular approach to every issue. In doing so she dispels any notion that this is yet another book about ‘whingeing nurses’. In the first of three sections she covers Nurses and Doctors at Work and explores all the well-worn themes in a fresh and lively style. Gordon's approach to the second section, The Media and Nursing, is fairly novel. She starts with how children are imbued with certain stereotypes of nurses through books at the beginning of their lives. She goes onto adult forms of indoctrination (for example, Nurse Ratched in One Flew over the Cuckoo's Nest and Sarah Gamp in Martin Chuzzlewit) and then examines nurses in TV and film. In the third and most important section, Hospitals and Nursing, Gordon is at her best when writing about the impact of cost cutting and hospital re-structuring on nursing. A typical example comes from her own experience. When asked by a critical care journal to write about patient-centred care, she found that the nurses interviewed were less than enthusiastic, even hostile, to the concept. Gordon soon discovered why: I didn’t have to dig far before I discovered that patient-centred or patient-focused care was a central building stone in the restructuring and reengineering of health care. It was one of those Orwellian formulations used to describe its opposite. Under the guise of patient-centred, patient-focused care, nurses quickly told me, they were losing their ability to centre on the patient and were increasingly asked to focus on profit (p. 229). In her Conclusion, Changing the Odds, Gordon considers the various options available to address the current crisis in nursing, whilst recognizing that many of nurses’ would-be solutions are ‘off limits’. In these final pages Gordon underestimates the immense political forces in the USA that oppose any change to its inequitable and hugely wasteful health care system (think of Hillary Clinton failing dismally to achieve health care reform during her husband's presidency). She also tends to view the rest of the world through somewhat rose-tinted spectacles. But these are small complaints about an immensely courageous book.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.904
Threshold uncertainty score0.803

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.054
GPT teacher head0.461
Teacher spread0.407 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations4
Published2005
Admission routes1
Has abstractyes

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