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Record W2136053437 · doi:10.1186/1472-6947-12-84

Tool, weapon, or white elephant? A realist analysis of the five phases of a twenty-year programme of occupational health information system implementation in the health sector

2012· article· en· W2136053437 on OpenAlexafffundabout
Jerry Spiegel, Karen Lockhart, Carmen Dyck, Andrea Wilson, Lyndsay M. O’Hara, Annalee Yassi

Bibliographic record

VenueBMC Medical Informatics and Decision Making · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicInformation Systems Theories and Implementation
Canadian institutionsSpinal Cord Injury BCUniversity of British Columbia
FundersCanadian Institutes of Health ResearchUniversity of British Columbia
KeywordsHealth informaticsPublic relationsWorkforceHealth careHealth services researchStaffingFraming (construction)Context (archaeology)BusinessPolitical scienceEconomic growthMedicineEconomicsNursingEngineering

Abstract

fetched live from OpenAlex

BACKGROUND: Although information systems (IS) have been extensively applied in the health sector worldwide, few initiatives have addressed the health and safety of health workers, a group acknowledged to be at high risk of injury and illness, as well as in great shortage globally, particularly in low and middle-income countries. METHODS: Adapting a context-mechanism-outcome case study design, we analyze our team's own experience over two decades to address this gap: in two different Canadian provinces; and two distinct South African settings. Applying a realist analysis within an adapted structuration theory framing sensitive to power relations, we explore contextual (socio-political and technological) characteristics and mechanisms affecting outcomes at micro, meso and macro levels. RESULTS: Technological limitations hindered IS usefulness in the initial Canadian locale, while staffing inadequacies amid pronounced power imbalances affecting governance restricted IS usefulness in the subsequent Canadian application. Implementation in South Africa highlighted the special care needed to address power dynamics regarding both worker-employer relations (relevant to all occupational health settings) and North-south imbalances (common to all international interactions). Researchers, managers and front-line workers all view IS implementation differently; relationships amongst the workplace parties and between community and academic partners have been pivotal in determining outcome in all circumstances. Capacity building and applying creative commons and open source solutions are showing promise, as is international collaboration. CONCLUSIONS: There is worldwide consensus on the need for IS use to protect the health workforce. However, IS implementation is a resource-intensive undertaking; regardless of how carefully designed the software, contextual factors and the mechanisms adopted to address these are critical to mitigate threats and achieve outcomes of interest to all parties. Issues specific to IS development, including technological support and software licensing models, can also affect outcome and sustainability - especially in the North-south context. Careful attention must be given to power relations between the various stakeholders at macro, meso and micro levels when implementing IS. North-South-South collaborations should be encouraged. Governance as well as technological issues are crucial determinants of IS application, and ultimately whether the system is seen as a tool, weapon, or white elephant by the various involved parties."You may call me a fool, But was there a rule The weapon should be turned into a tool? And what do we see? The first tool I step on Turned into a weapon. - Robert Frost""White (albino) elephants were regarded as holy in ancient times in Thailand and other Asian countries. Keeping a white elephant was a very expensive undertaking, since the owner had to provide the elephant with special food and provide access for people who wanted to worship it. If a Thai King became dissatisfied with a subordinate, he would give him a white elephant. The gift would, in most cases, ruin the recipient. - The Phrase Finder"

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.025
metaresearch head score (Gemma)0.043
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.992
Threshold uncertainty score0.473

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0250.043
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0040.006
Science and technology studies0.0080.011
Scholarly communication0.0070.004
Open science0.0030.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.083
GPT teacher head0.443
Teacher spread0.360 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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

Citations19
Published2012
Admission routes3
Has abstractyes

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